| <a href="depthwise__convolution_8cl.xhtml">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/*</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2019 ARM Limited.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * SPDX-License-Identifier: MIT</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> *</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * Permission is hereby granted, free of charge, to any person obtaining a copy</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * of this software and associated documentation files (the "Software"), to</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * deal in the Software without restriction, including without limitation the</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * sell copies of the Software, and to permit persons to whom the Software is</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * furnished to do so, subject to the following conditions:</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> *</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * The above copyright notice and this permission notice shall be included in all</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> * copies or substantial portions of the Software.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> *</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * SOFTWARE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> */</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="preprocessor">#include "<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml">helpers.h</a>"</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> </div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="preprocessor">#include "<a class="code" href="activation__float__helpers_8h.xhtml">activation_float_helpers.h</a>"</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment"></span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="comment">/** Get the pointer position at a certain offset in x and y direction.</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="comment"> *</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="comment"> * @param[in] ptr Pointer to the starting position of the buffer</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="comment"> * @param[in] x Relative X position</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="comment"> * @param[in] y Relative Y position</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="comment"> * @param[in] stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="comment"> * @param[in] stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="comment"> *</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="comment"> * @return a uchar</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="comment"> */</span></div><div class="line"><a name="l00038"></a><span class="lineno"><a class="line" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450"> 38</a></span> <span class="keyword">inline</span> __global uchar *<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(__global uchar *ptr, <span class="keyword">const</span> <span class="keywordtype">int</span> x, <span class="keyword">const</span> <span class="keywordtype">int</span> y, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_x, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_y)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="keywordflow">return</span> ptr + x * stride_x + y * stride_y;</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> }</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> </div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0, weights_row0) \</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="preprocessor"> acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="preprocessor"> acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> <span class="preprocessor"> acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> <span class="preprocessor"> acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span> <span class="preprocessor"> acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span> <span class="preprocessor"> acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST4X1_STRIDE1(acc, src0, weights_row0) \</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> <span class="preprocessor"> acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> <span class="preprocessor"> acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> <span class="preprocessor"> acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span> <span class="preprocessor"> acc.s1 = fma(src0.s1, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="preprocessor"> acc.s1 = fma(src0.s2, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span> <span class="preprocessor"> acc.s1 = fma(src0.s3, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span> <span class="preprocessor"> acc.s2 = fma(src0.s2, weights_row0.s0, acc.s2); \</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span> <span class="preprocessor"> acc.s2 = fma(src0.s3, weights_row0.s1, acc.s2); \</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span> <span class="preprocessor"> acc.s2 = fma(src0.s4, weights_row0.s2, acc.s2); \</span></div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> <span class="preprocessor"> acc.s3 = fma(src0.s3, weights_row0.s0, acc.s3); \</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> <span class="preprocessor"> acc.s3 = fma(src0.s4, weights_row0.s1, acc.s3); \</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span> <span class="preprocessor"> acc.s3 = fma(src0.s5, weights_row0.s2, acc.s3); \</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST2X1_STRIDE2(acc, src0, src1, weights_row0) \</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> <span class="preprocessor"> acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="preprocessor"> acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="preprocessor"> acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="preprocessor"> acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="preprocessor"> acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="preprocessor"> acc.s1 = fma(src1.s0, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST4X1_STRIDE2(acc, src0, src1, weights_row0) \</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="preprocessor"> acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="preprocessor"> acc.s0 = fma(src0.s1, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="preprocessor"> acc.s0 = fma(src0.s2, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> <span class="preprocessor"> acc.s1 = fma(src0.s2, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span> <span class="preprocessor"> acc.s1 = fma(src0.s3, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> <span class="preprocessor"> acc.s1 = fma(src0.s4, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span> <span class="preprocessor"> acc.s2 = fma(src0.s4, weights_row0.s0, acc.s2); \</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> <span class="preprocessor"> acc.s2 = fma(src0.s5, weights_row0.s1, acc.s2); \</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span> <span class="preprocessor"> acc.s2 = fma(src0.s6, weights_row0.s2, acc.s2); \</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> <span class="preprocessor"> acc.s3 = fma(src0.s6, weights_row0.s0, acc.s3); \</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> <span class="preprocessor"> acc.s3 = fma(src0.s7, weights_row0.s1, acc.s3); \</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> <span class="preprocessor"> acc.s3 = fma(src1.s0, weights_row0.s2, acc.s3); \</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span> </div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> </div><div class="line"><a name="l00099"></a><span class="lineno"><a class="line" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44"> 99</a></span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \</span></div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span> <span class="preprocessor"> acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="preprocessor"> acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span> <span class="preprocessor"> acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> <span class="preprocessor"> acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> <span class="preprocessor"> acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span> <span class="preprocessor"> acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"><a class="line" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44"> 109</a></span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST2X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="preprocessor"> acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="preprocessor"> acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="preprocessor"> acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="preprocessor"> acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="preprocessor"> acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> <span class="preprocessor"> acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"><a class="line" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa"> 119</a></span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST4X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="preprocessor"> acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="preprocessor"> acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="preprocessor"> acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <span class="preprocessor"> acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="preprocessor"> acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="preprocessor"> acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="preprocessor"> acc.s2 = fma(src0_left.s2, weights_row0.s0, acc.s2); \</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="preprocessor"> acc.s2 = fma(src0_mid.s2, weights_row0.s1, acc.s2); \</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="preprocessor"> acc.s2 = fma(src0_right.s2, weights_row0.s2, acc.s2); \</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="preprocessor"> acc.s3 = fma(src0_left.s3, weights_row0.s0, acc.s3); \</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="preprocessor"> acc.s3 = fma(src0_mid.s3, weights_row0.s1, acc.s3); \</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="preprocessor"> acc.s3 = fma(src0_right.s3, weights_row0.s2, acc.s3); \</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> </div><div class="line"><a name="l00135"></a><span class="lineno"><a class="line" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc"> 135</a></span> <span class="preprocessor">#define CONVOLUTION1x3_BIFROST4X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="preprocessor"> acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="preprocessor"> acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> <span class="preprocessor"> acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="preprocessor"> acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="preprocessor"> acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="preprocessor"> acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="preprocessor"> acc.s2 = fma(src0_left.s4, weights_row0.s0, acc.s2); \</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="preprocessor"> acc.s2 = fma(src0_mid.s4, weights_row0.s1, acc.s2); \</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="preprocessor"> acc.s2 = fma(src0_right.s4, weights_row0.s2, acc.s2); \</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="preprocessor"> acc.s3 = fma(src0_left.s6, weights_row0.s0, acc.s3); \</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="preprocessor"> acc.s3 = fma(src0_mid.s6, weights_row0.s1, acc.s3); \</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="preprocessor"> acc.s3 = fma(src0_right.s6, weights_row0.s2, acc.s3); \</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> </div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="preprocessor">#if defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F32)</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="preprocessor">#if defined(CONV_STRIDE_X)</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="preprocessor">#if CONV_STRIDE_X == 1</span></div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="preprocessor">#define convolution1x3 convolution1x3_stride_1</span></div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> <span class="preprocessor">#elif CONV_STRIDE_X == 2</span></div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span> <span class="preprocessor">#define convolution1x3 convolution1x3_stride_2</span></div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span> <span class="preprocessor">#elif CONV_STRIDE_X == 3</span></div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span> <span class="preprocessor">#define convolution1x3 convolution1x3_stride_3</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> <span class="preprocessor">#else </span><span class="comment">/* CONV_STRIDE_X */</span><span class="preprocessor"></span></div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span> <span class="preprocessor">#error "Stride not supported"</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> <span class="preprocessor">#endif </span><span class="comment">/* CONV_STRIDE_X */</span><span class="preprocessor"></span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> <span class="comment"></span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span> <span class="comment">/** Compute a 1D horizontal convolution of size 3 and stride 1 for floating point type.</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> <span class="comment"> *</span></div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel.</span></div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> <span class="comment"> * @param[in] left_coeff Weight of the left pixel</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span> <span class="comment"> * @param[in] right_coeff Weight of the right pixel</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="comment"> *</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span> <span class="comment"> * @return a float2 containing 2 convoluted values.</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> <span class="comment"> */</span></div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span> <span class="keyword">inline</span> float2 convolution1x3_stride_1(__global <span class="keyword">const</span> uchar *left_pixel,</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> left_coeff,</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> middle_coeff,</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> right_coeff)</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> {</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  float4 temp = vload4(0, (__global <span class="keywordtype">float</span> *)left_pixel);</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  float2 left = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s01, float2);</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  float2 middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s12, float2);</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  float2 right = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s23, float2);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keywordflow">return</span> left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;</div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">return</span> vload2(0, (__global <span class="keywordtype">float</span> *)left_pixel) * (float2)left_coeff</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  + vload2(0, (__global <span class="keywordtype">float</span> *)(left_pixel) + DILATION_X) * (float2)middle_coeff</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  + vload2(0, (__global <span class="keywordtype">float</span> *)(left_pixel) + 2 * DILATION_X) * (float2)right_coeff;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span> }</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> <span class="comment"></span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="comment">/** Compute a 1D horizontal convolution of size 3 and stride 2 for floating point type.</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> <span class="comment"> *</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel.</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"> * @param[in] left_coeff Weight of the left pixel</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="comment"> * @param[in] right_coeff Weight of the right pixel</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span> <span class="comment"> *</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> <span class="comment"> * @return a float2 containing 2 convoluted values.</span></div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span> <span class="comment"> */</span></div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="keyword">inline</span> float2 convolution1x3_stride_2(__global <span class="keyword">const</span> uchar *left_pixel,</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> left_coeff,</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> middle_coeff,</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> right_coeff)</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> {</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  float4 temp0 = vload4(0, (__global <span class="keywordtype">float</span> *)left_pixel);</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordtype">float</span> temp1 = *((__global <span class="keywordtype">float</span> *)(left_pixel + 4 * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>)));</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  float2 left = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s02, float2);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  float2 middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s13, float2);</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  float2 right = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((float2)(temp0.s2, temp1), float2);</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span> </div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keywordflow">return</span> left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  __global <span class="keywordtype">float</span> *left_pixel_float = (__global <span class="keywordtype">float</span> *)left_pixel;</div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> </div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  <span class="keywordflow">return</span> vload4(0, left_pixel_float).s02 * (float2)left_coeff</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  + vload4(0, left_pixel_float + DILATION_X).s02 * (float2)middle_coeff</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  + vload4(0, left_pixel_float + DILATION_X * 2).s02 * (float2)right_coeff;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> }</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="comment"></span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="comment">/** Compute a 1D horizontal convolution of size 3 and stride 3 for floating point type.</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="comment"> *</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel.</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="comment"> * @param[in] left_coeff Weight of the left pixel</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="comment"> * @param[in] right_coeff Weight of the right pixel</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span> <span class="comment"> *</span></div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> <span class="comment"> * @return a float2 containing 2 convoluted values.</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span> <span class="comment"> */</span></div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> <span class="keyword">inline</span> float2 convolution1x3_stride_3(__global <span class="keyword">const</span> uchar *left_pixel,</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> left_coeff,</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> middle_coeff,</div><div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> right_coeff)</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</span> {</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  float4 temp0 = vload4(0, (__global <span class="keywordtype">float</span> *)left_pixel);</div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  float2 temp1 = vload2(0, (__global <span class="keywordtype">float</span> *)(left_pixel + 4 * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>)));</div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> </div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  float2 left = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s03, float2);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  float2 middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((float2)(temp0.s1, temp1.s0), float2);</div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  float2 right = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((float2)(temp0.s2, temp1.s1), float2);</div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> </div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="keywordflow">return</span> left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff;</div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  __global <span class="keywordtype">float</span> *left_pixel_float = (__global <span class="keywordtype">float</span> *)left_pixel;</div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> </div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keywordflow">return</span> (float2)(*left_pixel_float, *(left_pixel_float + 3)) * (float2)left_coeff</div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  + (float2)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3)) * (float2)middle_coeff</div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  + (float2)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3)) * (float2)right_coeff;</div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> }</div><div class="line"><a name="l00258"></a><span class="lineno"> 258</span> <span class="comment"></span></div><div class="line"><a name="l00259"></a><span class="lineno"> 259</span> <span class="comment">/** Apply a 3x3 convolution matrix to a single channel F32 input image and return the result.</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="comment"> *</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</span> <span class="comment"> * Convolution matrix layout:</span></div><div class="line"><a name="l00262"></a><span class="lineno"> 262</span> <span class="comment"> *</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</span> <span class="comment"> * [ mat0, mat1, mat2 ]\n</span></div><div class="line"><a name="l00264"></a><span class="lineno"> 264</span> <span class="comment"> * [ mat3, mat4, mat5 ]\n</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="comment"> * [ mat6, mat7, mat8 ]\n</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="comment"> *</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</span> <span class="comment"> * @param[in] src A pointer to source Image structure</span></div><div class="line"><a name="l00268"></a><span class="lineno"> 268</span> <span class="comment"> * @param[in] mat0 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</span> <span class="comment"> * @param[in] mat1 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00270"></a><span class="lineno"> 270</span> <span class="comment"> * @param[in] mat2 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="comment"> * @param[in] mat3 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00272"></a><span class="lineno"> 272</span> <span class="comment"> * @param[in] mat4 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00273"></a><span class="lineno"> 273</span> <span class="comment"> * @param[in] mat5 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> <span class="comment"> * @param[in] mat6 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00275"></a><span class="lineno"> 275</span> <span class="comment"> * @param[in] mat0 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00276"></a><span class="lineno"> 276</span> <span class="comment"> * @param[in] mat7 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00277"></a><span class="lineno"> 277</span> <span class="comment"> * @param[in] mat8 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> <span class="comment"> *</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span> <span class="comment"> * @return a float2 containing 2 convoluted values.</span></div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span> <span class="comment"> */</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span> <span class="keyword">inline</span> float2 <a class="code" href="convolution3x3_8cl.xhtml#afc5fefe72e66f0ae5191fd5b708fade9">convolution3x3</a>(</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  __global <span class="keyword">const</span> uchar *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>,</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> src_stride_y,</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> mat0, <span class="keyword">const</span> <span class="keywordtype">float</span> mat1, <span class="keyword">const</span> <span class="keywordtype">float</span> mat2,</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> mat3, <span class="keyword">const</span> <span class="keywordtype">float</span> mat4, <span class="keyword">const</span> <span class="keywordtype">float</span> mat5,</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <span class="keywordtype">float</span> mat6, <span class="keyword">const</span> <span class="keywordtype">float</span> mat7, <span class="keyword">const</span> <span class="keywordtype">float</span> mat8)</div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> {</div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  float2 pixels;</div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span> </div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  pixels = <a class="code" href="convolution3x3_8cl.xhtml#a92702074338198e81a46c3e309d9b04f">convolution1x3</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> + 0 * DILATION_Y * src_stride_y), mat0, mat1, mat2);</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  pixels += <a class="code" href="convolution3x3_8cl.xhtml#a92702074338198e81a46c3e309d9b04f">convolution1x3</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> + 1 * DILATION_Y * src_stride_y), mat3, mat4, mat5);</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  pixels += <a class="code" href="convolution3x3_8cl.xhtml#a92702074338198e81a46c3e309d9b04f">convolution1x3</a>((<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> + 2 * DILATION_Y * src_stride_y), mat6, mat7, mat8);</div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span> </div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  <span class="keywordflow">return</span> pixels;</div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span> }</div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span> <span class="comment"></span></div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="comment">/** This OpenCL kernel computes the depthwise convolution 3x3</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span> <span class="comment"> *</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> <span class="comment"> *</span></div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00310"></a><span class="lineno"> 310</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32</span></div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00312"></a><span class="lineno"> 312</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00314"></a><span class="lineno"> 314</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector</span></div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F16/F32</span></div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span> <span class="comment"> */</span></div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3(</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l00336"></a><span class="lineno"> 336</span>  ,</div><div class="line"><a name="l00337"></a><span class="lineno"> 337</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> #endif <span class="comment">//defined(HAS_BIAS)</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span> )</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> {</div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span> </div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  float2 pixels = 0.0f;</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> </div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span>  <span class="comment">// Extract channel and linearized batch indices</span></div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel = get_global_id(2) % DST_CHANNELS;</div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / DST_CHANNELS;</div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span>  <span class="comment">// Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> </div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span>  __global uchar *weights_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z;</div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> </div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span>  __global uchar *src_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z;</div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> </div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span>  float3 weights_values0 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span>  float3 weights_values1 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span>  float3 weights_values2 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> </div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span>  pixels = <a class="code" href="convolution3x3_8cl.xhtml#afc5fefe72e66f0ae5191fd5b708fade9">convolution3x3</a>(src_addr, src_stride_y,</div><div class="line"><a name="l00362"></a><span class="lineno"> 362</span>  weights_values0.s0, weights_values0.s1, weights_values0.s2,</div><div class="line"><a name="l00363"></a><span class="lineno"> 363</span>  weights_values1.s0, weights_values1.s1, weights_values1.s2,</div><div class="line"><a name="l00364"></a><span class="lineno"> 364</span>  weights_values2.s0, weights_values2.s1, weights_values2.s2);</div><div class="line"><a name="l00365"></a><span class="lineno"> 365</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l00366"></a><span class="lineno"> 366</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l00367"></a><span class="lineno"> 367</span> </div><div class="line"><a name="l00368"></a><span class="lineno"> 368</span>  <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> = *((__global <span class="keywordtype">float</span> *)(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&biases, channel)));</div><div class="line"><a name="l00369"></a><span class="lineno"> 369</span> </div><div class="line"><a name="l00370"></a><span class="lineno"> 370</span>  pixels += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00371"></a><span class="lineno"> 371</span> <span class="preprocessor">#endif //defined(HAS_BIAS)</span></div><div class="line"><a name="l00372"></a><span class="lineno"> 372</span> </div><div class="line"><a name="l00373"></a><span class="lineno"> 373</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr);</div><div class="line"><a name="l00374"></a><span class="lineno"> 374</span> }</div><div class="line"><a name="l00375"></a><span class="lineno"> 375</span> <span class="preprocessor">#endif //defined(CONV_STRIDE_X)</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</span> </div><div class="line"><a name="l00377"></a><span class="lineno"> 377</span> <span class="preprocessor">#if(DILATION_X > 1 || DILATION_Y > 1)</span></div><div class="line"><a name="l00378"></a><span class="lineno"> 378</span> <span class="comment"></span></div><div class="line"><a name="l00379"></a><span class="lineno"> 379</span> <span class="comment">/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 or DILATION_Y>1 for F32</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> <span class="comment"> *</span></div><div class="line"><a name="l00381"></a><span class="lineno"> 381</span> <span class="comment"> * @param[in] src_addr Pointer to the starting position of where to perform the convolution</span></div><div class="line"><a name="l00382"></a><span class="lineno"> 382</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00383"></a><span class="lineno"> 383</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00384"></a><span class="lineno"> 384</span> <span class="comment"> * @param[in] y_offset Offset from the source tensor from which to start convolution</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</span> <span class="comment"> * @param[in] weights_addr Pointer from where to get weights</span></div><div class="line"><a name="l00386"></a><span class="lineno"> 386</span> <span class="comment"> * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension</span></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span> <span class="comment"> */</span></div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span> <span class="keyword">inline</span> float2 convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(__global uchar *src_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_x_bytes, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_y_bytes,</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y_offset, __global uchar *weights_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> weights_stride_y)</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span> {</div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  float3 weights_row0 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  float3 weights_row1 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span>  float3 weights_row2 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span> </div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  float2 pixels0 = 0.0f;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span> </div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span>  float2 src00_left = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row0</span></div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  float2 src00_mid = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span>  float2 src00_right = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span> </div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  float2 src10_left = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row1</span></div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span>  float2 src10_mid = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  float2 src10_right = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> </div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  float2 src20_left = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row2</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span>  float2 src20_mid = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span>  float2 src20_right = vload2(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span> </div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels0, src00_left, src00_mid, src00_right, weights_row0);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels0, src10_left, src10_mid, src10_right, weights_row1);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels0, src20_left, src20_mid, src20_right, weights_row2);</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span> </div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span>  <span class="keywordflow">return</span> pixels0;</div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span> }</div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span> <span class="comment"></span></div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span> <span class="comment">/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 or DILATION_Y>1 for F32</span></div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span> <span class="comment"> *</span></div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span> <span class="comment"> * @param[in] src_addr Pointer to the starting position of where to perform the convolution</span></div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> <span class="comment"> * @param[in] y_offset Offset from the source tensor from which to start convolution</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> <span class="comment"> * @param[in] weights_addr Pointer from where to get weights</span></div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span> <span class="comment"> * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span> <span class="comment"> */</span></div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> <span class="keyword">inline</span> float2 convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(__global uchar *src_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_x_bytes, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_y_bytes,</div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y_offset, __global uchar *weights_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> weights_stride_y)</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> {</div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span>  float3 weights_row0 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span>  float3 weights_row1 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  float3 weights_row2 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</span> </div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  float2 pixels0 = 0.0f;</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span> </div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  float3 src00_left = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row0</span></div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span>  float3 src00_mid = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  float3 src00_right = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00439"></a><span class="lineno"> 439</span> </div><div class="line"><a name="l00440"></a><span class="lineno"> 440</span>  float3 src10_left = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row1</span></div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  float3 src10_mid = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  float3 src10_right = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span> </div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span>  float3 src20_left = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row2</span></div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span>  float3 src20_mid = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  float3 src20_right = vload3(0, (__global <span class="keywordtype">float</span> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span> </div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels0, src00_left, src00_mid, src00_right, weights_row0);</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels0, src10_left, src10_mid, src10_right, weights_row1);</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels0, src20_left, src20_mid, src20_right, weights_row2);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span> </div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span>  <span class="keywordflow">return</span> pixels0;</div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span> }</div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span> </div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span> <span class="preprocessor">#endif </span><span class="comment">/* (DILATION_X > 1 || DILATION_Y > 1) */</span><span class="preprocessor"></span></div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span> <span class="comment"></span></div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span> <span class="comment">/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both</span></div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> <span class="comment"> * stride_x and stride_y are equal to 1</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> <span class="comment"> *</span></div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span> <span class="comment"> * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float.</span></div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> <span class="comment"> *</span></div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32</span></div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00470"></a><span class="lineno"> 470</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32</span></div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00477"></a><span class="lineno"> 477</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32</span></div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector</span></div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F32</span></div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span> <span class="comment"> */</span></div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32(</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  ,</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> #endif <span class="comment">//defined(HAS_BIAS)</span></div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span> )</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span> {</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</span> </div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  float2 pixels0 = 0.0f;</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  float2 pixels1 = 0.0f;</div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  float2 pixels2 = 0.0f;</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span>  float2 pixels3 = 0.0f;</div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span> </div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span>  <span class="comment">// Extract channel and linearized batch indices</span></div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel = get_global_id(2) % DST_CHANNELS;</div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / DST_CHANNELS;</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  <span class="comment">// Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  __global uchar *weights_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z;</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span>  __global uchar *src_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z;</div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span> </div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span>  float3 weights_row0 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span>  float3 weights_row1 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  float3 weights_row2 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span> </div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span>  <span class="comment">// Note: Since each work-item computes 4x2 elements, we need to load 6 rows from the input tensor</span></div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  float4 src00 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 0 * src_stride_y)); <span class="comment">// Row0</span></div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  float4 src10 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 1 * src_stride_y)); <span class="comment">// Row1</span></div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span>  float4 src20 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 2 * src_stride_y)); <span class="comment">// Row2</span></div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span>  float4 src30 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 3 * src_stride_y)); <span class="comment">// Row3</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span>  float4 src40 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 4 * src_stride_y)); <span class="comment">// Row4</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  float4 src50 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 5 * src_stride_y)); <span class="comment">// Row5</span></div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> </div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels0, src00, weights_row0);</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels0, src10, weights_row1);</div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels0, src20, weights_row2);</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels1, src10, weights_row0);</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels1, src20, weights_row1);</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels1, src30, weights_row2);</div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels2, src20, weights_row0);</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels2, src30, weights_row1);</div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels2, src40, weights_row2);</div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels3, src30, weights_row0);</div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels3, src40, weights_row1);</div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#ae40b0f25b3985d4853b944151ffddb44">CONVOLUTION1x3_BIFROST2X1_STRIDE1</a>(pixels3, src50, weights_row2);</div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> </div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> </div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span>  <span class="comment">//3x3 Convolution of elements starting in 0th row</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span>  pixels0 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 0, weights_addr, weights_stride_y);</div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span>  <span class="comment">//3x3 Convolution of elements starting in 1st row</span></div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span>  pixels1 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 1, weights_addr, weights_stride_y);</div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span>  <span class="comment">//3x3 Convolution of elements starting in 2nd row</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</span>  pixels2 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 2, weights_addr, weights_stride_y);</div><div class="line"><a name="l00555"></a><span class="lineno"> 555</span>  <span class="comment">//3x3 Convolution of elements starting in 3rd row</span></div><div class="line"><a name="l00556"></a><span class="lineno"> 556</span>  pixels3 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 3, weights_addr, weights_stride_y);</div><div class="line"><a name="l00557"></a><span class="lineno"> 557</span> </div><div class="line"><a name="l00558"></a><span class="lineno"> 558</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00559"></a><span class="lineno"> 559</span> </div><div class="line"><a name="l00560"></a><span class="lineno"> 560</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00561"></a><span class="lineno"> 561</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l00562"></a><span class="lineno"> 562</span> </div><div class="line"><a name="l00563"></a><span class="lineno"> 563</span>  <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> = *((__global <span class="keywordtype">float</span> *)(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&biases, channel)));</div><div class="line"><a name="l00564"></a><span class="lineno"> 564</span> </div><div class="line"><a name="l00565"></a><span class="lineno"> 565</span>  pixels0 += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00566"></a><span class="lineno"> 566</span>  pixels1 += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00567"></a><span class="lineno"> 567</span>  pixels2 += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00568"></a><span class="lineno"> 568</span>  pixels3 += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00569"></a><span class="lineno"> 569</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l00570"></a><span class="lineno"> 570</span> </div><div class="line"><a name="l00571"></a><span class="lineno"> 571</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels0, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 0 * dst_stride_y));</div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels1, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 1 * dst_stride_y));</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels2, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 2 * dst_stride_y));</div><div class="line"><a name="l00574"></a><span class="lineno"> 574</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels3, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 3 * dst_stride_y));</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</span> }</div><div class="line"><a name="l00576"></a><span class="lineno"> 576</span> <span class="comment"></span></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</span> <span class="comment">/** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both</span></div><div class="line"><a name="l00578"></a><span class="lineno"> 578</span> <span class="comment"> * stride_x and stride_y are equal to 2</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span> <span class="comment"> *</span></div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> <span class="comment"> * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float.</span></div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span> <span class="comment"> *</span></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: F32</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F32</span></div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector</span></div><div class="line"><a name="l00609"></a><span class="lineno"> 609</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F32</span></div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l00611"></a><span class="lineno"> 611</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> <span class="comment"> */</span></div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32(</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l00619"></a><span class="lineno"> 619</span>  ,</div><div class="line"><a name="l00620"></a><span class="lineno"> 620</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span> #endif <span class="comment">//defined(HAS_BIAS)</span></div><div class="line"><a name="l00622"></a><span class="lineno"> 622</span> )</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span> {</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span> </div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  float2 pixels0 = 0.0f;</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span>  float2 pixels1 = 0.0f;</div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span> </div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  <span class="comment">// Extract channel and linearized batch indices</span></div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel = get_global_id(2) % DST_CHANNELS;</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / DST_CHANNELS;</div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  <span class="comment">// Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l00635"></a><span class="lineno"> 635</span>  __global uchar *weights_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z;</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  __global uchar *src_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z;</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</span> </div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00639"></a><span class="lineno"> 639</span> </div><div class="line"><a name="l00640"></a><span class="lineno"> 640</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  float3 weights_row0 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span>  float3 weights_row1 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  float3 weights_row2 = vload3(0, (__global <span class="keywordtype">float</span> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span> </div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  <span class="comment">// Note: Since each work-item computes 4x2 elements, we need to load 5 rows from the input tensor</span></div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span>  float4 src00 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 0 * src_stride_y)); <span class="comment">// Row0</span></div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  float2 src01 = vload2(2, (__global <span class="keywordtype">float</span> *)(src_addr + 0 * src_stride_y)); <span class="comment">// Row0</span></div><div class="line"><a name="l00648"></a><span class="lineno"> 648</span>  float4 src10 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 1 * src_stride_y)); <span class="comment">// Row1</span></div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  float2 src11 = vload2(2, (__global <span class="keywordtype">float</span> *)(src_addr + 1 * src_stride_y)); <span class="comment">// Row1</span></div><div class="line"><a name="l00650"></a><span class="lineno"> 650</span>  float4 src20 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 2 * src_stride_y)); <span class="comment">// Row2</span></div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  float2 src21 = vload2(2, (__global <span class="keywordtype">float</span> *)(src_addr + 2 * src_stride_y)); <span class="comment">// Row2</span></div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span>  float4 src30 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 3 * src_stride_y)); <span class="comment">// Row3</span></div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  float2 src31 = vload2(2, (__global <span class="keywordtype">float</span> *)(src_addr + 3 * src_stride_y)); <span class="comment">// Row3</span></div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  float4 src40 = vload4(0, (__global <span class="keywordtype">float</span> *)(src_addr + 4 * src_stride_y)); <span class="comment">// Row4</span></div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span>  float2 src41 = vload2(2, (__global <span class="keywordtype">float</span> *)(src_addr + 4 * src_stride_y)); <span class="comment">// Row4</span></div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span> </div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels0, src00, src01, weights_row0);</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels0, src10, src11, weights_row1);</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels0, src20, src21, weights_row2);</div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels1, src20, src21, weights_row0);</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels1, src30, src31, weights_row1);</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a2da35283a28c35fd9f8b0d534e5a5a44">CONVOLUTION1x3_BIFROST2X1_STRIDE2</a>(pixels1, src40, src41, weights_row2);</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</span> </div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00665"></a><span class="lineno"> 665</span> </div><div class="line"><a name="l00666"></a><span class="lineno"> 666</span>  <span class="comment">//3x3 Convolution of elements starting in 0th row</span></div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 0, weights_addr, weights_stride_y);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span>  <span class="comment">//3x3 Convolution of elements starting in 2nd row</span></div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 2, weights_addr, weights_stride_y);</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00671"></a><span class="lineno"> 671</span> </div><div class="line"><a name="l00672"></a><span class="lineno"> 672</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</span> </div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  <span class="keywordtype">float</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> = *((__global <span class="keywordtype">float</span> *)(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&biases, channel)));</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</span> </div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  pixels0 += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span>  pixels1 += (float2)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span> </div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels0, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 0 * dst_stride_y));</div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  vstore2(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels1, A_VAL, B_VAL), 0, (__global <span class="keywordtype">float</span> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 1 * dst_stride_y));</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span> }</div><div class="line"><a name="l00684"></a><span class="lineno"> 684</span> </div><div class="line"><a name="l00685"></a><span class="lineno"> 685</span> <span class="preprocessor">#endif // defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F32)</span></div><div class="line"><a name="l00686"></a><span class="lineno"> 686</span> </div><div class="line"><a name="l00687"></a><span class="lineno"> 687</span> <span class="preprocessor">#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DST_WIDTH)</span></div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span> <span class="comment">/** Reshape the weights for quantized depthwise convolution</span></div><div class="line"><a name="l00689"></a><span class="lineno"> 689</span> <span class="comment"> *</span></div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type, e.g. -DDATA_TYPE=uint8</span></div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> <span class="comment"> * @note Output width should be given as a preprocessor argument using -DDST_WIDTH=width, e.g. -DDST_WIDTH=128</span></div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=vec_size, e.g., -DVEC_SIZE=4</span></div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span> <span class="comment"> * @attention Input's height and width should be 3</span></div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> <span class="comment"> *</span></div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8</span></div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00700"></a><span class="lineno"> 700</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00702"></a><span class="lineno"> 702</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span> <span class="comment"> * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span> <span class="comment"> */</span></div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_reshape_weights(</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a22f42fcf2077d951271df83b55c1a71a">IMAGE_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>))</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</span> {</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  <a class="code" href="struct_vector.xhtml">Vector</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a527bfdf5eeb306f1cf01c4a8e29f38e0">CONVERT_TO_VECTOR_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> x = get_global_id(0);</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span> </div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span>  <span class="comment">// Load 3x3xVEC_SIZE weights</span></div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  w0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 0 * src_stride_y + 0 * src_stride_z);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  w1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 1 * src_stride_y + 0 * src_stride_z);</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  w2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 2 * src_stride_y + 0 * src_stride_z);</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  w3 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 0 * src_stride_y + 1 * src_stride_z);</div><div class="line"><a name="l00726"></a><span class="lineno"> 726</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  w4 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 1 * src_stride_y + 1 * src_stride_z);</div><div class="line"><a name="l00728"></a><span class="lineno"> 728</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  w5 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 2 * src_stride_y + 1 * src_stride_z);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  w6 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 0 * src_stride_y + 2 * src_stride_z);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span>  w7 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 1 * src_stride_y + 2 * src_stride_z);</div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  w8 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr + 2 * src_stride_y + 2 * src_stride_z);</div><div class="line"><a name="l00736"></a><span class="lineno"> 736</span> </div><div class="line"><a name="l00737"></a><span class="lineno"> 737</span>  __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * DST_WIDTH * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>);</div><div class="line"><a name="l00738"></a><span class="lineno"> 738</span> </div><div class="line"><a name="l00739"></a><span class="lineno"> 739</span> <span class="preprocessor">#if defined(TRANSPOSE)</span></div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span> <span class="preprocessor">#if VEC_SIZE != 4</span></div><div class="line"><a name="l00741"></a><span class="lineno"> 741</span> <span class="preprocessor">#error "VEC_SIZE not supported"</span></div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span> <span class="preprocessor">#else // VEC_SIZE != 4</span></div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w0.s0, w1.s0, w2.s0, w3.s0), 0, dst_addr + 0);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w4.s0, w5.s0, w6.s0, w7.s0), 0, dst_addr + 1 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w8.s0, w0.s1, w1.s1, w2.s1), 0, dst_addr + 2 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w3.s1, w4.s1, w5.s1, w6.s1), 0, dst_addr + 3 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w7.s1, w8.s1, w0.s2, w1.s2), 0, dst_addr + 4 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w2.s2, w3.s2, w4.s2, w5.s2), 0, dst_addr + 5 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w6.s2, w7.s2, w8.s2, w0.s3), 0, dst_addr + 6 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w1.s3, w2.s3, w3.s3, w4.s3), 0, dst_addr + 7 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span>  ((<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>))(w5.s3, w6.s3, w7.s3, w8.s3), 0, dst_addr + 8 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span> <span class="preprocessor">#endif // VEC_SIZE != 4</span></div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> <span class="preprocessor">#else // !defined(TRANSPOSE)</span></div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span>  (w0, 0, dst_addr + 0);</div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span>  (w1, 0, dst_addr + 1 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span>  (w2, 0, dst_addr + 2 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span>  (w3, 0, dst_addr + 3 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span>  (w4, 0, dst_addr + 4 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00773"></a><span class="lineno"> 773</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00774"></a><span class="lineno"> 774</span>  (w5, 0, dst_addr + 5 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00775"></a><span class="lineno"> 775</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00776"></a><span class="lineno"> 776</span>  (w6, 0, dst_addr + 6 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00777"></a><span class="lineno"> 777</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00778"></a><span class="lineno"> 778</span>  (w7, 0, dst_addr + 7 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00779"></a><span class="lineno"> 779</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l00780"></a><span class="lineno"> 780</span>  (w8, 0, dst_addr + 8 * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>);</div><div class="line"><a name="l00781"></a><span class="lineno"> 781</span> <span class="preprocessor">#endif // defined(TRANSPOSE)</span></div><div class="line"><a name="l00782"></a><span class="lineno"> 782</span> }</div><div class="line"><a name="l00783"></a><span class="lineno"> 783</span> <span class="preprocessor">#endif // defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DST_WIDTH)</span></div><div class="line"><a name="l00784"></a><span class="lineno"> 784</span> </div><div class="line"><a name="l00785"></a><span class="lineno"> 785</span> <span class="preprocessor">#if defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16)</span></div><div class="line"><a name="l00786"></a><span class="lineno"> 786</span> <span class="preprocessor">#if defined(CONV_STRIDE_X)</span></div><div class="line"><a name="l00787"></a><span class="lineno"> 787</span> <span class="preprocessor">#if CONV_STRIDE_X == 1</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</span> <span class="preprocessor">#define convolution1x3_f16 convolution1x3_stride_1_f16</span></div><div class="line"><a name="l00789"></a><span class="lineno"> 789</span> <span class="preprocessor">#elif CONV_STRIDE_X == 2</span></div><div class="line"><a name="l00790"></a><span class="lineno"> 790</span> <span class="preprocessor">#define convolution1x3_f16 convolution1x3_stride_2_f16</span></div><div class="line"><a name="l00791"></a><span class="lineno"> 791</span> <span class="preprocessor">#elif CONV_STRIDE_X == 3</span></div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> <span class="preprocessor">#define convolution1x3_f16 convolution1x3_stride_3_f16</span></div><div class="line"><a name="l00793"></a><span class="lineno"> 793</span> <span class="preprocessor">#else </span><span class="comment">/* CONV_STRIDE_X */</span><span class="preprocessor"></span></div><div class="line"><a name="l00794"></a><span class="lineno"> 794</span> <span class="preprocessor">#error "Stride not supported"</span></div><div class="line"><a name="l00795"></a><span class="lineno"> 795</span> <span class="preprocessor">#endif </span><span class="comment">/* CONV_STRIDE_X */</span><span class="preprocessor"></span></div><div class="line"><a name="l00796"></a><span class="lineno"> 796</span> </div><div class="line"><a name="l00797"></a><span class="lineno"> 797</span> <span class="preprocessor">#if(DILATION_X > 1 || DILATION_Y > 1)</span></div><div class="line"><a name="l00798"></a><span class="lineno"> 798</span> <span class="comment"></span></div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span> <span class="comment">/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 or DILATION_Y>1 for f16</span></div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span> <span class="comment"> *</span></div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> <span class="comment"> * @param[in] src_addr Pointer to the starting position of where to perform the convolution</span></div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span> <span class="comment"> * @param[in] y_offset Offset from the source tensor from which to start convolution</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span> <span class="comment"> * @param[in] weights_addr Pointer from where to get weights</span></div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> <span class="comment"> * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension</span></div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span> <span class="comment"> */</span></div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span> <span class="keyword">inline</span> half4 convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(__global uchar *src_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_x_bytes, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_y_bytes,</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y_offset, __global uchar *weights_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> weights_stride_y)</div><div class="line"><a name="l00810"></a><span class="lineno"> 810</span> {</div><div class="line"><a name="l00811"></a><span class="lineno"> 811</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span>  half3 weights_row0 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  half3 weights_row1 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  half3 weights_row2 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> </div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  half4 pixels0 = 0.0f;</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> </div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  half4 src00_left = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row0</span></div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span>  half4 src00_mid = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  half4 src00_right = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span> </div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  half4 src10_left = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row1</span></div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span>  half4 src10_mid = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  half4 src10_right = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span> </div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span>  half4 src20_left = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row2</span></div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  half4 src20_mid = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  half4 src20_right = vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00829"></a><span class="lineno"> 829</span> </div><div class="line"><a name="l00830"></a><span class="lineno"> 830</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels0, src00_left, src00_mid, src00_right, weights_row0);</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels0, src10_left, src10_mid, src10_right, weights_row1);</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels0, src20_left, src20_mid, src20_right, weights_row2);</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</span> </div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  <span class="keywordflow">return</span> pixels0;</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span> }</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span> <span class="comment"></span></div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span> <span class="comment">/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 or DILATION_Y>1 for F16</span></div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span> <span class="comment"> *</span></div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span> <span class="comment"> * @param[in] src_addr Pointer to the starting position of where to perform the convolution</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span> <span class="comment"> * @param[in] y_offset Offset from the source tensor from which to start convolution</span></div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> <span class="comment"> * @param[in] weights_addr Pointer from where to get weights</span></div><div class="line"><a name="l00844"></a><span class="lineno"> 844</span> <span class="comment"> * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension</span></div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span> <span class="comment"> */</span></div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span> <span class="keyword">inline</span> half4 convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(__global uchar *src_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_x_bytes, <span class="keyword">const</span> <span class="keywordtype">int</span> stride_y_bytes,</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> y_offset, __global uchar *weights_addr, <span class="keyword">const</span> <span class="keywordtype">int</span> weights_stride_y)</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> {</div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  half3 weights_row0 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span>  half3 weights_row1 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span>  half3 weights_row2 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> </div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  half4 pixels0 = 0.0f;</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span> </div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  half8 src00_left = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row0</span></div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span>  half8 src00_mid = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  half8 src00_right = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span> </div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  half8 src10_left = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row1</span></div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span>  half8 src10_mid = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span>  half8 src10_right = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span> </div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span>  half8 src20_left = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); <span class="comment">// Row2</span></div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span>  half8 src20_mid = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span>  half8 src20_right = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="depthwise__convolution_8cl.xhtml#a4201b7aeda129409f16dd5a5cfe56450">ptr_offset</a>(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes));</div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> </div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels0, src00_left, src00_mid, src00_right, weights_row0);</div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels0, src10_left, src10_mid, src10_right, weights_row1);</div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels0, src20_left, src20_mid, src20_right, weights_row2);</div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span> </div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span>  <span class="keywordflow">return</span> pixels0;</div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span> }</div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span> </div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> <span class="preprocessor">#endif // (DILATION_X > 1 && DILATION_Y > 1)</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> <span class="comment"></span></div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> <span class="comment">/** Compute a 1D horizontal convolution of size 3 and stride 1 for 16bit floating point type.</span></div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span> <span class="comment"> *</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel.</span></div><div class="line"><a name="l00880"></a><span class="lineno"> 880</span> <span class="comment"> * @param[in] left_coeff Weight of the left pixel</span></div><div class="line"><a name="l00881"></a><span class="lineno"> 881</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00882"></a><span class="lineno"> 882</span> <span class="comment"> * @param[in] right_coeff Weight of the right pixel</span></div><div class="line"><a name="l00883"></a><span class="lineno"> 883</span> <span class="comment"> *</span></div><div class="line"><a name="l00884"></a><span class="lineno"> 884</span> <span class="comment"> * @return a half4 containing 4 convoluted values.</span></div><div class="line"><a name="l00885"></a><span class="lineno"> 885</span> <span class="comment"> */</span></div><div class="line"><a name="l00886"></a><span class="lineno"> 886</span> <span class="keyword">inline</span> half4 convolution1x3_stride_1_f16(__global <span class="keyword">const</span> uchar *left_pixel,</div><div class="line"><a name="l00887"></a><span class="lineno"> 887</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> left_coeff,</div><div class="line"><a name="l00888"></a><span class="lineno"> 888</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> middle_coeff,</div><div class="line"><a name="l00889"></a><span class="lineno"> 889</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> right_coeff)</div><div class="line"><a name="l00890"></a><span class="lineno"> 890</span> {</div><div class="line"><a name="l00891"></a><span class="lineno"> 891</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00892"></a><span class="lineno"> 892</span> </div><div class="line"><a name="l00893"></a><span class="lineno"> 893</span>  half8 temp = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)left_pixel);</div><div class="line"><a name="l00894"></a><span class="lineno"> 894</span> </div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span>  half4 left = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s0123, half4);</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</span>  half4 middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s1234, half4);</div><div class="line"><a name="l00897"></a><span class="lineno"> 897</span>  half4 right = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s2345, half4);</div><div class="line"><a name="l00898"></a><span class="lineno"> 898</span> </div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  <span class="keywordflow">return</span> left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff;</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keywordflow">return</span> vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)left_pixel) * (half4)left_coeff</div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  + vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(left_pixel) + DILATION_X) * (half4)middle_coeff</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  + vload4(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(left_pixel) + 2 * DILATION_X) * (half4)right_coeff;</div><div class="line"><a name="l00904"></a><span class="lineno"> 904</span> </div><div class="line"><a name="l00905"></a><span class="lineno"> 905</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span> }</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span> <span class="comment"></span></div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> <span class="comment">/** Compute a 1D horizontal convolution of size 3 and stride 2 for 16bit floating point type.</span></div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span> <span class="comment"> *</span></div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel.</span></div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span> <span class="comment"> * @param[in] left_coeff Weight of the left pixel</span></div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span> <span class="comment"> * @param[in] right_coeff Weight of the right pixel</span></div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span> <span class="comment"> *</span></div><div class="line"><a name="l00915"></a><span class="lineno"> 915</span> <span class="comment"> * @return a half4 containing 4 convoluted values.</span></div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span> <span class="comment"> */</span></div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span> <span class="keyword">inline</span> half4 convolution1x3_stride_2_f16(__global <span class="keyword">const</span> uchar *left_pixel,</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> left_coeff,</div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> middle_coeff,</div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> right_coeff)</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> {</div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00923"></a><span class="lineno"> 923</span> </div><div class="line"><a name="l00924"></a><span class="lineno"> 924</span>  half8 temp0 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)left_pixel);</div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> temp1 = *((__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(left_pixel + 8 * <span class="keyword">sizeof</span>(<a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a>)));</div><div class="line"><a name="l00926"></a><span class="lineno"> 926</span> </div><div class="line"><a name="l00927"></a><span class="lineno"> 927</span>  half4 left = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s0246, half4);</div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  half4 middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s1357, half4);</div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  half4 right = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((half4)(temp0.s246, temp1), half4);</div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span> </div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  <span class="keywordflow">return</span> left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff;</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span> </div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span>  __global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *left_pixel_float = (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)left_pixel;</div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span> </div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span>  <span class="keywordflow">return</span> (half4)(*left_pixel_float, *(left_pixel_float + 2), *(left_pixel_float + 4), *(left_pixel_float + 6)) * (half4)left_coeff</div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span>  + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 2), *(left_pixel_float + DILATION_X + 4), *(left_pixel_float + DILATION_X + 6)) * (half4)middle_coeff</div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 2), *(left_pixel_float + DILATION_X * 2 + 4), *(left_pixel_float + DILATION_X * 2 + 6)) * (half4)right_coeff;</div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span> </div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span> }</div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span> <span class="comment"></span></div><div class="line"><a name="l00943"></a><span class="lineno"> 943</span> <span class="comment">/** Compute a 1D horizontal convolution of size 3 and stride 3 for 16bit floating point type.</span></div><div class="line"><a name="l00944"></a><span class="lineno"> 944</span> <span class="comment"> *</span></div><div class="line"><a name="l00945"></a><span class="lineno"> 945</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel.</span></div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> <span class="comment"> * @param[in] left_coeff Weight of the left pixel</span></div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span> <span class="comment"> * @param[in] right_coeff Weight of the right pixel</span></div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span> <span class="comment"> *</span></div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span> <span class="comment"> * @return a half4 containing 4 convoluted values.</span></div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span> <span class="comment"> */</span></div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> <span class="keyword">inline</span> half4 convolution1x3_stride_3_f16(__global <span class="keyword">const</span> uchar *left_pixel,</div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> left_coeff,</div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> middle_coeff,</div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> right_coeff)</div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span> {</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span> </div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span>  half16 temp0 = vload16(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)left_pixel);</div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span> </div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span>  half4 left = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s0369, half4);</div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span>  half4 middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s147A, half4);</div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  half4 right = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp0.s258B, half4);</div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span> </div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span>  <span class="keywordflow">return</span> left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff;</div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span> </div><div class="line"><a name="l00968"></a><span class="lineno"> 968</span>  __global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *left_pixel_float = (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)left_pixel;</div><div class="line"><a name="l00969"></a><span class="lineno"> 969</span> </div><div class="line"><a name="l00970"></a><span class="lineno"> 970</span>  <span class="keywordflow">return</span> (half4)(*left_pixel_float, *(left_pixel_float + 3), *(left_pixel_float + 6), *(left_pixel_float + 9)) * (half4)left_coeff</div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span>  + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3), *(left_pixel_float + DILATION_X + 6), *(left_pixel_float + DILATION_X + 9)) * (half4)middle_coeff</div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3), *(left_pixel_float + DILATION_X * 2 + 6), *(left_pixel_float + DILATION_X * 2 + 9)) * (half4)right_coeff;</div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span> </div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span> }</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span> <span class="comment"></span></div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span> <span class="comment">/** Apply a 3x3 convolution matrix to a single channel F16 input image and return the result.</span></div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span> <span class="comment"> *</span></div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span> <span class="comment"> * Convolution matrix layout:</span></div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span> <span class="comment"> *</span></div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span> <span class="comment"> * [ mat0, mat1, mat2 ]\n</span></div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> <span class="comment"> * [ mat3, mat4, mat5 ]\n</span></div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span> <span class="comment"> * [ mat6, mat7, mat8 ]\n</span></div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span> <span class="comment"> *</span></div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span> <span class="comment"> * @param[in] src A pointer to source Image structure</span></div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span> <span class="comment"> * @param[in] mat0 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span> <span class="comment"> * @param[in] mat1 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span> <span class="comment"> * @param[in] mat2 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span> <span class="comment"> * @param[in] mat3 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> <span class="comment"> * @param[in] mat4 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span> <span class="comment"> * @param[in] mat5 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span> <span class="comment"> * @param[in] mat6 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00993"></a><span class="lineno"> 993</span> <span class="comment"> * @param[in] mat0 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00994"></a><span class="lineno"> 994</span> <span class="comment"> * @param[in] mat7 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00995"></a><span class="lineno"> 995</span> <span class="comment"> * @param[in] mat8 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> <span class="comment"> *</span></div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span> <span class="comment"> * @return a half4 containing 4 convoluted values.</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span> <span class="comment"> */</span></div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span> <span class="keyword">inline</span> half4 convolution3x3_f16(</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  <a class="code" href="struct_image.xhtml">Image</a> *<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>,</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat0, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat1, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat2,</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat3, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat4, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat5,</div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>  <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat6, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat7, <span class="keyword">const</span> <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> mat8)</div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span> {</div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  half4 pixels;</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span> </div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  pixels = convolution1x3_f16(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>, 0, 0), mat0, mat1, mat2);</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  pixels += convolution1x3_f16(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>, 0, DILATION_Y), mat3, mat4, mat5);</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  pixels += convolution1x3_f16(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>, 0, DILATION_Y * 2), mat6, mat7, mat8);</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span> </div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <span class="keywordflow">return</span> pixels;</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span> }</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span> </div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span> <span class="preprocessor">#if defined(DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span> <span class="comment"></span></div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span> <span class="comment">/** This OpenCL kernel computes the depthwise convolution 3x3</span></div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span> <span class="comment"> *</span></div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span> <span class="comment"> * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half.</span></div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span> <span class="comment"> *</span></div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16</span></div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector</span></div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: F16</span></div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> <span class="comment"> */</span></div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_f16(</div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span>  ,</div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> #endif <span class="comment">//defined(HAS_BIAS)</span></div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span> )</div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> {</div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> <span class="preprocessor">#endif //defined(HAS_BIAS)</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> </div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span>  <span class="comment">// Extract channel and linearized batch indices</span></div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel = get_global_id(2) % DST_CHANNELS;</div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / DST_CHANNELS;</div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span>  <span class="comment">// Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr -= batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z + (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z;</div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span>  __global uchar *weights_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z;</div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> </div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span>  uchar3 <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = (uchar3)(0, 1, 2) * (uchar3)weights_stride_y;</div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span>  half3 weights_values0 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span>  half3 weights_values1 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span>  half3 weights_values2 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> </div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span>  half4 pixels = convolution3x3_f16(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>, weights_values0.s0, weights_values0.s1, weights_values0.s2,</div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span>  weights_values1.s0, weights_values1.s1, weights_values1.s2,</div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span>  weights_values2.s0, weights_values2.s1, weights_values2.s2);</div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span>  pixels += (half4)(*((__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(biases.<a class="code" href="struct_vector.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a> + channel * biases_stride_x)));</div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span> <span class="preprocessor">#endif //defined(HAS_BIAS)</span></div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span> </div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr);</div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span> }</div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> <span class="preprocessor">#endif // defined(DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span> <span class="preprocessor">#endif // defined(CONV_STRIDE_X)</span></div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> <span class="comment"></span></div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> <span class="comment">/** This OpenCL kernel is optimized for Bifrost architectures and computes the 16bit floating point depthwise convolution 3x3</span></div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> <span class="comment"> * when both stride_x and stride_y are equal to 1</span></div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> <span class="comment"> *</span></div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> <span class="comment"> * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half.</span></div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> <span class="comment"> *</span></div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16</span></div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector</span></div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l01129"></a><span class="lineno"> 1129</span> <span class="comment"> */</span></div><div class="line"><a name="l01130"></a><span class="lineno"> 1130</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16(</div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l01132"></a><span class="lineno"> 1132</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l01133"></a><span class="lineno"> 1133</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l01134"></a><span class="lineno"> 1134</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l01135"></a><span class="lineno"> 1135</span>  ,</div><div class="line"><a name="l01136"></a><span class="lineno"> 1136</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</span> #endif <span class="comment">//defined(HAS_BIAS)</span></div><div class="line"><a name="l01138"></a><span class="lineno"> 1138</span> )</div><div class="line"><a name="l01139"></a><span class="lineno"> 1139</span> {</div><div class="line"><a name="l01140"></a><span class="lineno"> 1140</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l01141"></a><span class="lineno"> 1141</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l01142"></a><span class="lineno"> 1142</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l01143"></a><span class="lineno"> 1143</span> </div><div class="line"><a name="l01144"></a><span class="lineno"> 1144</span>  <span class="comment">// Extract channel and linearized batch indices</span></div><div class="line"><a name="l01145"></a><span class="lineno"> 1145</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel = get_global_id(2) % DST_CHANNELS;</div><div class="line"><a name="l01146"></a><span class="lineno"> 1146</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / DST_CHANNELS;</div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span> </div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01149"></a><span class="lineno"> 1149</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l01150"></a><span class="lineno"> 1150</span> </div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> = *((__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&biases, channel)));</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span> </div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  half4 pixels0 = 0.0f;</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  half4 pixels1 = 0.0f;</div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span>  half4 pixels2 = 0.0f;</div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  half4 pixels3 = 0.0f;</div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span> </div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <span class="comment">// Load relevant input and weights data (Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span>  __global uchar *weights_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z;</div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  __global uchar *src_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z;</div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span> </div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  half3 weights_row0 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  half3 weights_row1 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  half3 weights_row2 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l01168"></a><span class="lineno"> 1168</span> </div><div class="line"><a name="l01169"></a><span class="lineno"> 1169</span>  <span class="comment">// Note: Since each work-item computes 4x4 elements, we need to load 6 rows from the input tensor</span></div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  half8 src00 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 0 * src_stride_y)); <span class="comment">// Row0</span></div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  half8 src10 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 1 * src_stride_y)); <span class="comment">// Row1</span></div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span>  half8 src20 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 2 * src_stride_y)); <span class="comment">// Row2</span></div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span>  half8 src30 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 3 * src_stride_y)); <span class="comment">// Row3</span></div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span>  half8 src40 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 4 * src_stride_y)); <span class="comment">// Row4</span></div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  half8 src50 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 5 * src_stride_y)); <span class="comment">// Row5</span></div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span> </div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels0, src00, weights_row0);</div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels0, src10, weights_row1);</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels0, src20, weights_row2);</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels1, src10, weights_row0);</div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels1, src20, weights_row1);</div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels1, src30, weights_row2);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels2, src20, weights_row0);</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels2, src30, weights_row1);</div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels2, src40, weights_row2);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels3, src30, weights_row0);</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels3, src40, weights_row1);</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#a0916b921e5c01cc64afede6dc7bd5caa">CONVOLUTION1x3_BIFROST4X1_STRIDE1</a>(pixels3, src50, weights_row2);</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span> </div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span> </div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  <span class="comment">//3x3 Convolution of elements starting in 0th row</span></div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  pixels0 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 0, weights_addr, weights_stride_y);</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span>  <span class="comment">//3x3 Convolution of elements starting in 1st row</span></div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span>  pixels1 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 1, weights_addr, weights_stride_y);</div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <span class="comment">//3x3 Convolution of elements starting in 2nd row</span></div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  pixels2 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 2, weights_addr, weights_stride_y);</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  <span class="comment">//3x3 Convolution of elements starting in 3rd row</span></div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  pixels3 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 3, weights_addr, weights_stride_y);</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> </div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span> </div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span>  pixels0 += (half4)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span>  pixels1 += (half4)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span>  pixels2 += (half4)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span>  pixels3 += (half4)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span> </div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels0, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 0 * dst_stride_y));</div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels1, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 1 * dst_stride_y));</div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels2, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 2 * dst_stride_y));</div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels3, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 3 * dst_stride_y));</div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> }</div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> <span class="comment"></span></div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</span> <span class="comment">/** This OpenCL kernel is optimized for Bifrost architectures and computes 16bit floating point the depthwise convolution 3x3</span></div><div class="line"><a name="l01217"></a><span class="lineno"> 1217</span> <span class="comment"> * when both stride_x and stride_y are equal to 2</span></div><div class="line"><a name="l01218"></a><span class="lineno"> 1218</span> <span class="comment"> *</span></div><div class="line"><a name="l01219"></a><span class="lineno"> 1219</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l01220"></a><span class="lineno"> 1220</span> <span class="comment"> * @note If activation function is enabled, the data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types: half.</span></div><div class="line"><a name="l01221"></a><span class="lineno"> 1221</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l01223"></a><span class="lineno"> 1223</span> <span class="comment"> *</span></div><div class="line"><a name="l01224"></a><span class="lineno"> 1224</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16</span></div><div class="line"><a name="l01225"></a><span class="lineno"> 1225</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01226"></a><span class="lineno"> 1226</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01227"></a><span class="lineno"> 1227</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01228"></a><span class="lineno"> 1228</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01229"></a><span class="lineno"> 1229</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01230"></a><span class="lineno"> 1230</span> <span class="comment"> * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l01231"></a><span class="lineno"> 1231</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01234"></a><span class="lineno"> 1234</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01235"></a><span class="lineno"> 1235</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the biases vector</span></div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as @p src_ptr</span></div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span> <span class="comment"> */</span></div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16(</div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  ,</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span> #endif <span class="comment">//defined(HAS_BIAS)</span></div><div class="line"><a name="l01261"></a><span class="lineno"> 1261</span> )</div><div class="line"><a name="l01262"></a><span class="lineno"> 1262</span> {</div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  <a class="code" href="struct_image.xhtml">Image</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a541f8db866a0fa93ee67d58ea31a7d0c">CONVERT_TENSOR3D_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span>  <a class="code" href="struct_tensor3_d.xhtml">Tensor3D</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a79e8e562daa6599317d2d1cd86ef1bf2">CONVERT_TO_TENSOR3D_STRUCT_NO_STEP</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span> </div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <span class="comment">// Extract channel and linearized batch indices</span></div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> channel = get_global_id(2) % DST_CHANNELS;</div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / DST_CHANNELS;</div><div class="line"><a name="l01270"></a><span class="lineno"> 1270</span> </div><div class="line"><a name="l01271"></a><span class="lineno"> 1271</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a64d779f80eeb923e0ab2313433f7b40b">CONVERT_TO_VECTOR_STRUCT_NO_STEP</a>(biases);</div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span> </div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a> = *((__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a7e4940407322d6f0ccb8b6b86b856019">vector_offset</a>(&biases, channel)));</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span> </div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  half4 pixels0 = 0.0f;</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  half4 pixels1 = 0.0f;</div><div class="line"><a name="l01279"></a><span class="lineno"> 1279</span> </div><div class="line"><a name="l01280"></a><span class="lineno"> 1280</span>  <span class="comment">// Load relevant input and weights data ( Accounts depth multiplier when indexing input, OFM = IFM * DEPTH_MULTIPLIER)</span></div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  __global uchar *weights_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z;</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  __global uchar *src_addr = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z;</div><div class="line"><a name="l01283"></a><span class="lineno"> 1283</span> </div><div class="line"><a name="l01284"></a><span class="lineno"> 1284</span> <span class="preprocessor">#if(DILATION_X == 1 && DILATION_Y == 1)</span></div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span> </div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  <span class="comment">// Load the weights</span></div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  half3 weights_row0 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 0 * weights_stride_y));</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  half3 weights_row1 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 1 * weights_stride_y));</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span>  half3 weights_row2 = vload3(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(weights_addr + 2 * weights_stride_y));</div><div class="line"><a name="l01290"></a><span class="lineno"> 1290</span> </div><div class="line"><a name="l01291"></a><span class="lineno"> 1291</span>  <span class="comment">// Note: Since each work-item computes 2x4 elements, we need to load 5 rows from the input tensor</span></div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span>  half8 src00 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 0 * src_stride_y)); <span class="comment">// Row0</span></div><div class="line"><a name="l01293"></a><span class="lineno"> 1293</span>  half2 src01 = vload2(4, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 0 * src_stride_y)); <span class="comment">// Row0</span></div><div class="line"><a name="l01294"></a><span class="lineno"> 1294</span>  half8 src10 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 1 * src_stride_y)); <span class="comment">// Row1</span></div><div class="line"><a name="l01295"></a><span class="lineno"> 1295</span>  half2 src11 = vload2(4, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 1 * src_stride_y)); <span class="comment">// Row1</span></div><div class="line"><a name="l01296"></a><span class="lineno"> 1296</span>  half8 src20 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 2 * src_stride_y)); <span class="comment">// Row2</span></div><div class="line"><a name="l01297"></a><span class="lineno"> 1297</span>  half2 src21 = vload2(4, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 2 * src_stride_y)); <span class="comment">// Row2</span></div><div class="line"><a name="l01298"></a><span class="lineno"> 1298</span>  half8 src30 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 3 * src_stride_y)); <span class="comment">// Row3</span></div><div class="line"><a name="l01299"></a><span class="lineno"> 1299</span>  half2 src31 = vload2(4, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 3 * src_stride_y)); <span class="comment">// Row3</span></div><div class="line"><a name="l01300"></a><span class="lineno"> 1300</span>  half8 src40 = vload8(0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 4 * src_stride_y)); <span class="comment">// Row4</span></div><div class="line"><a name="l01301"></a><span class="lineno"> 1301</span>  half2 src41 = vload2(4, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(src_addr + 4 * src_stride_y)); <span class="comment">// Row4</span></div><div class="line"><a name="l01302"></a><span class="lineno"> 1302</span> </div><div class="line"><a name="l01303"></a><span class="lineno"> 1303</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels0, src00, src01, weights_row0);</div><div class="line"><a name="l01304"></a><span class="lineno"> 1304</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels0, src10, src11, weights_row1);</div><div class="line"><a name="l01305"></a><span class="lineno"> 1305</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels0, src20, src21, weights_row2);</div><div class="line"><a name="l01306"></a><span class="lineno"> 1306</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels1, src20, src21, weights_row0);</div><div class="line"><a name="l01307"></a><span class="lineno"> 1307</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels1, src30, src31, weights_row1);</div><div class="line"><a name="l01308"></a><span class="lineno"> 1308</span>  <a class="code" href="depthwise__convolution_8cl.xhtml#aa18ba8a4892890c942fea83c5cad8dbc">CONVOLUTION1x3_BIFROST4X1_STRIDE2</a>(pixels1, src40, src41, weights_row2);</div><div class="line"><a name="l01309"></a><span class="lineno"> 1309</span> </div><div class="line"><a name="l01310"></a><span class="lineno"> 1310</span> <span class="preprocessor">#else </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l01311"></a><span class="lineno"> 1311</span>  <span class="comment">//3x3 Convolution of elements starting in 0th row</span></div><div class="line"><a name="l01312"></a><span class="lineno"> 1312</span>  pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 0, weights_addr, weights_stride_y);</div><div class="line"><a name="l01313"></a><span class="lineno"> 1313</span>  <span class="comment">//3x3 Convolution of elements starting in 2nd row</span></div><div class="line"><a name="l01314"></a><span class="lineno"> 1314</span>  pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_x, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>.stride_y, 2, weights_addr, weights_stride_y);</div><div class="line"><a name="l01315"></a><span class="lineno"> 1315</span> <span class="preprocessor">#endif </span><span class="comment">/* DILATION_X==1 && DILATION_Y==1 */</span><span class="preprocessor"></span></div><div class="line"><a name="l01316"></a><span class="lineno"> 1316</span> </div><div class="line"><a name="l01317"></a><span class="lineno"> 1317</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01318"></a><span class="lineno"> 1318</span>  pixels0 += (half4)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l01319"></a><span class="lineno"> 1319</span>  pixels1 += (half4)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a3a77be8aebd8e00522b32061d46ccdbd">bias</a>;</div><div class="line"><a name="l01320"></a><span class="lineno"> 1320</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(HAS_BIAS) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01321"></a><span class="lineno"> 1321</span> </div><div class="line"><a name="l01322"></a><span class="lineno"> 1322</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels0, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 0 * dst_stride_y));</div><div class="line"><a name="l01323"></a><span class="lineno"> 1323</span>  vstore4(<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, pixels1, A_VAL, B_VAL), 0, (__global <a class="code" href="namespacearm__compute.xhtml#a73e2825fd61d349c5ca2f5313e3c8ea1">half</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr + 1 * dst_stride_y));</div><div class="line"><a name="l01324"></a><span class="lineno"> 1324</span> }</div><div class="line"><a name="l01325"></a><span class="lineno"> 1325</span> <span class="preprocessor">#endif // defined(ARM_COMPUTE_OPENCL_FP16_ENABLED) && defined(DEPTH_MULTIPLIER) && defined(DST_CHANNELS) && defined(IS_F16)</span></div><div class="line"><a name="l01326"></a><span class="lineno"> 1326</span> </div><div class="line"><a name="l01327"></a><span class="lineno"> 1327</span> <span class="preprocessor">#if defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP)</span></div><div class="line"><a name="l01328"></a><span class="lineno"> 1328</span> <span class="comment">/** This function computes the depthwise convolution for NHWC data layout. This kernel assumes that the weights tensor is NOT reshaped</span></div><div class="line"><a name="l01329"></a><span class="lineno"> 1329</span> <span class="comment"> *</span></div><div class="line"><a name="l01330"></a><span class="lineno"> 1330</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l01331"></a><span class="lineno"> 1331</span> <span class="comment"> * @note The number of elements processed must be passed at compile time using -DN0 (e.g. -DN0=2)</span></div><div class="line"><a name="l01332"></a><span class="lineno"> 1332</span> <span class="comment"> * @note The depth multiplier must be passed at compile time using -DDEPTH_MULTIPLIER (e.g. -DDEPTH_MULTIPLIER=1)</span></div><div class="line"><a name="l01333"></a><span class="lineno"> 1333</span> <span class="comment"> * @note The first dimension of the input tensor must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM1=112)</span></div><div class="line"><a name="l01334"></a><span class="lineno"> 1334</span> <span class="comment"> * @note The second dimension of the input tensor must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM2=80)</span></div><div class="line"><a name="l01335"></a><span class="lineno"> 1335</span> <span class="comment"> * @note The kernel width must be passed at compile time using -DKERNEL_WIDTH (e.g. -DKERNEL_WIDTH=5)</span></div><div class="line"><a name="l01336"></a><span class="lineno"> 1336</span> <span class="comment"> * @note The kernel height must be passed at compile time using -DKERNEL_HEIGHT (e.g. -DKERNEL_HEIGHT=5)</span></div><div class="line"><a name="l01337"></a><span class="lineno"> 1337</span> <span class="comment"> * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1)</span></div><div class="line"><a name="l01338"></a><span class="lineno"> 1338</span> <span class="comment"> * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1)</span></div><div class="line"><a name="l01339"></a><span class="lineno"> 1339</span> <span class="comment"> * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X)</span></div><div class="line"><a name="l01340"></a><span class="lineno"> 1340</span> <span class="comment"> * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1)</span></div><div class="line"><a name="l01341"></a><span class="lineno"> 1341</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l01342"></a><span class="lineno"> 1342</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l01343"></a><span class="lineno"> 1343</span> <span class="comment"> *</span></div><div class="line"><a name="l01344"></a><span class="lineno"> 1344</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32</span></div><div class="line"><a name="l01345"></a><span class="lineno"> 1345</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01346"></a><span class="lineno"> 1346</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01347"></a><span class="lineno"> 1347</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01348"></a><span class="lineno"> 1348</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01349"></a><span class="lineno"> 1349</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01350"></a><span class="lineno"> 1350</span> <span class="comment"> * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l01351"></a><span class="lineno"> 1351</span> <span class="comment"> * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)</span></div><div class="line"><a name="l01352"></a><span class="lineno"> 1352</span> <span class="comment"> * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)</span></div><div class="line"><a name="l01353"></a><span class="lineno"> 1353</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l01354"></a><span class="lineno"> 1354</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr</span></div><div class="line"><a name="l01355"></a><span class="lineno"> 1355</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01356"></a><span class="lineno"> 1356</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01357"></a><span class="lineno"> 1357</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01358"></a><span class="lineno"> 1358</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01359"></a><span class="lineno"> 1359</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01360"></a><span class="lineno"> 1360</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01361"></a><span class="lineno"> 1361</span> <span class="comment"> * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes)</span></div><div class="line"><a name="l01362"></a><span class="lineno"> 1362</span> <span class="comment"> * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes)</span></div><div class="line"><a name="l01363"></a><span class="lineno"> 1363</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l01364"></a><span class="lineno"> 1364</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32</span></div><div class="line"><a name="l01365"></a><span class="lineno"> 1365</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01366"></a><span class="lineno"> 1366</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01367"></a><span class="lineno"> 1367</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01368"></a><span class="lineno"> 1368</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01369"></a><span class="lineno"> 1369</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01370"></a><span class="lineno"> 1370</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01371"></a><span class="lineno"> 1371</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor</span></div><div class="line"><a name="l01372"></a><span class="lineno"> 1372</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr</span></div><div class="line"><a name="l01373"></a><span class="lineno"> 1373</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l01374"></a><span class="lineno"> 1374</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01375"></a><span class="lineno"> 1375</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l01376"></a><span class="lineno"> 1376</span> <span class="comment"> */</span></div><div class="line"><a name="l01377"></a><span class="lineno"> 1377</span> __kernel <span class="keywordtype">void</span> dwc_MxN_native_fp_nhwc(</div><div class="line"><a name="l01378"></a><span class="lineno"> 1378</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l01379"></a><span class="lineno"> 1379</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l01380"></a><span class="lineno"> 1380</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>)</div><div class="line"><a name="l01381"></a><span class="lineno"> 1381</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l01382"></a><span class="lineno"> 1382</span>  ,</div><div class="line"><a name="l01383"></a><span class="lineno"> 1383</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases)</div><div class="line"><a name="l01384"></a><span class="lineno"> 1384</span> #endif <span class="comment">// defined(HAS_BIAS)</span></div><div class="line"><a name="l01385"></a><span class="lineno"> 1385</span> )</div><div class="line"><a name="l01386"></a><span class="lineno"> 1386</span> {</div><div class="line"><a name="l01387"></a><span class="lineno"> 1387</span>  <span class="keywordtype">int</span> x = get_global_id(0); <span class="comment">// channels</span></div><div class="line"><a name="l01388"></a><span class="lineno"> 1388</span>  <span class="keywordtype">int</span> y = get_global_id(1); <span class="comment">// spatial coordinate x</span></div><div class="line"><a name="l01389"></a><span class="lineno"> 1389</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01390"></a><span class="lineno"> 1390</span>  <span class="keywordtype">int</span> z = get_global_id(2) % (int)DST_DEPTH; <span class="comment">// spatial coordinate y</span></div><div class="line"><a name="l01391"></a><span class="lineno"> 1391</span>  <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> = get_global_id(2) / (int)DST_DEPTH; <span class="comment">// batch</span></div><div class="line"><a name="l01392"></a><span class="lineno"> 1392</span> <span class="preprocessor">#else // defined(DST_DEPTH)</span></div><div class="line"><a name="l01393"></a><span class="lineno"> 1393</span>  <span class="keywordtype">int</span> z = get_global_id(2); <span class="comment">// spatial coordinate y</span></div><div class="line"><a name="l01394"></a><span class="lineno"> 1394</span> <span class="preprocessor">#endif // defined(DST_DEPTH)</span></div><div class="line"><a name="l01395"></a><span class="lineno"> 1395</span> </div><div class="line"><a name="l01396"></a><span class="lineno"> 1396</span>  __global uchar *s_addr = src_ptr + src_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * (<span class="keywordtype">int</span>)N0;</div><div class="line"><a name="l01397"></a><span class="lineno"> 1397</span> </div><div class="line"><a name="l01398"></a><span class="lineno"> 1398</span>  __global uchar *d_addr = dst_ptr + dst_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * (<span class="keywordtype">int</span>)DEPTH_MULTIPLIER * (int)N0 + y * dst_stride_y + z * dst_stride_z;</div><div class="line"><a name="l01399"></a><span class="lineno"> 1399</span> </div><div class="line"><a name="l01400"></a><span class="lineno"> 1400</span>  __global uchar *w_addr = weights_ptr + weights_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * (<span class="keywordtype">int</span>)DEPTH_MULTIPLIER * (int)N0;</div><div class="line"><a name="l01401"></a><span class="lineno"> 1401</span> </div><div class="line"><a name="l01402"></a><span class="lineno"> 1402</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01403"></a><span class="lineno"> 1403</span>  __global uchar *b_addr = biases_ptr + biases_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * (<span class="keywordtype">int</span>)DEPTH_MULTIPLIER * (int)N0;</div><div class="line"><a name="l01404"></a><span class="lineno"> 1404</span> <span class="preprocessor">#endif // defined(HAS_BIAS)</span></div><div class="line"><a name="l01405"></a><span class="lineno"> 1405</span> </div><div class="line"><a name="l01406"></a><span class="lineno"> 1406</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01407"></a><span class="lineno"> 1407</span>  s_addr += <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * src_stride_w;</div><div class="line"><a name="l01408"></a><span class="lineno"> 1408</span>  d_addr += <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * dst_stride_w;</div><div class="line"><a name="l01409"></a><span class="lineno"> 1409</span> <span class="preprocessor">#endif // defined(DST_DEPTH)</span></div><div class="line"><a name="l01410"></a><span class="lineno"> 1410</span> </div><div class="line"><a name="l01411"></a><span class="lineno"> 1411</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> d = 0; d < (int)DEPTH_MULTIPLIER; ++d)</div><div class="line"><a name="l01412"></a><span class="lineno"> 1412</span>  {</div><div class="line"><a name="l01413"></a><span class="lineno"> 1413</span>  <span class="comment">// Each work-item computes N0x1x1 elements</span></div><div class="line"><a name="l01414"></a><span class="lineno"> 1414</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, N0)</div><div class="line"><a name="l01415"></a><span class="lineno"> 1415</span>  res = 0;</div><div class="line"><a name="l01416"></a><span class="lineno"> 1416</span> </div><div class="line"><a name="l01417"></a><span class="lineno"> 1417</span>  <span class="keywordtype">int</span> x_coord = y * CONV_STRIDE_X - (int)CONV_PAD_LEFT;</div><div class="line"><a name="l01418"></a><span class="lineno"> 1418</span>  <span class="keywordtype">int</span> y_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP;</div><div class="line"><a name="l01419"></a><span class="lineno"> 1419</span> </div><div class="line"><a name="l01420"></a><span class="lineno"> 1420</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yk = 0; yk < KERNEL_HEIGHT; ++yk)</div><div class="line"><a name="l01421"></a><span class="lineno"> 1421</span>  {</div><div class="line"><a name="l01422"></a><span class="lineno"> 1422</span>  <span class="keywordflow">if</span>(y_coord >= 0 && y_coord < SRC_DIM2)</div><div class="line"><a name="l01423"></a><span class="lineno"> 1423</span>  {</div><div class="line"><a name="l01424"></a><span class="lineno"> 1424</span>  <span class="keywordtype">int</span> x_coord_tmp = x_coord;</div><div class="line"><a name="l01425"></a><span class="lineno"> 1425</span> </div><div class="line"><a name="l01426"></a><span class="lineno"> 1426</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xk = 0; xk < KERNEL_WIDTH; ++xk)</div><div class="line"><a name="l01427"></a><span class="lineno"> 1427</span>  {</div><div class="line"><a name="l01428"></a><span class="lineno"> 1428</span>  <span class="keywordflow">if</span>(x_coord_tmp >= 0 && x_coord_tmp < SRC_DIM1)</div><div class="line"><a name="l01429"></a><span class="lineno"> 1429</span>  {</div><div class="line"><a name="l01430"></a><span class="lineno"> 1430</span>  <span class="keywordtype">int</span> s_offset = x_coord_tmp * (int)src_stride_y + y_coord * (<span class="keywordtype">int</span>)src_stride_z;</div><div class="line"><a name="l01431"></a><span class="lineno"> 1431</span>  <span class="keywordtype">int</span> w_offset = xk * weights_stride_y + yk * weights_stride_z;</div><div class="line"><a name="l01432"></a><span class="lineno"> 1432</span> </div><div class="line"><a name="l01433"></a><span class="lineno"> 1433</span>  <span class="comment">// Load input and weights values</span></div><div class="line"><a name="l01434"></a><span class="lineno"> 1434</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, N0)</div><div class="line"><a name="l01435"></a><span class="lineno"> 1435</span>  i = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(N0)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(s_addr + s_offset));</div><div class="line"><a name="l01436"></a><span class="lineno"> 1436</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, N0)</div><div class="line"><a name="l01437"></a><span class="lineno"> 1437</span>  <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(N0)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(w_addr + w_offset));</div><div class="line"><a name="l01438"></a><span class="lineno"> 1438</span> </div><div class="line"><a name="l01439"></a><span class="lineno"> 1439</span> <span class="preprocessor">#if GPU_ARCH == GPU_ARCH_MIDGARD</span></div><div class="line"><a name="l01440"></a><span class="lineno"> 1440</span>  res += i * <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>;</div><div class="line"><a name="l01441"></a><span class="lineno"> 1441</span> <span class="preprocessor">#else // GPU_ARCH == GPU_ARCH_MIDGARD</span></div><div class="line"><a name="l01442"></a><span class="lineno"> 1442</span>  res = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(i, <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a1a367830ae09bf6138df822888ec1d71">w</a>, res);</div><div class="line"><a name="l01443"></a><span class="lineno"> 1443</span> <span class="preprocessor">#endif // GPU_ARCH == GPU_ARCH_MIDGARD</span></div><div class="line"><a name="l01444"></a><span class="lineno"> 1444</span>  }</div><div class="line"><a name="l01445"></a><span class="lineno"> 1445</span>  x_coord_tmp += DILATION_X;</div><div class="line"><a name="l01446"></a><span class="lineno"> 1446</span>  }</div><div class="line"><a name="l01447"></a><span class="lineno"> 1447</span>  }</div><div class="line"><a name="l01448"></a><span class="lineno"> 1448</span>  y_coord += DILATION_Y;</div><div class="line"><a name="l01449"></a><span class="lineno"> 1449</span>  }</div><div class="line"><a name="l01450"></a><span class="lineno"> 1450</span> </div><div class="line"><a name="l01451"></a><span class="lineno"> 1451</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01452"></a><span class="lineno"> 1452</span>  res += <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(N0)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(b_addr));</div><div class="line"><a name="l01453"></a><span class="lineno"> 1453</span> <span class="preprocessor">#endif // defined(HAS_BIAS)</span></div><div class="line"><a name="l01454"></a><span class="lineno"> 1454</span> </div><div class="line"><a name="l01455"></a><span class="lineno"> 1455</span>  res = <a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, res, A_VAL, B_VAL);</div><div class="line"><a name="l01456"></a><span class="lineno"> 1456</span> </div><div class="line"><a name="l01457"></a><span class="lineno"> 1457</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(N0)</div><div class="line"><a name="l01458"></a><span class="lineno"> 1458</span>  (res, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(d_addr));</div><div class="line"><a name="l01459"></a><span class="lineno"> 1459</span> </div><div class="line"><a name="l01460"></a><span class="lineno"> 1460</span>  w_addr += <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>);</div><div class="line"><a name="l01461"></a><span class="lineno"> 1461</span>  d_addr += <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>);</div><div class="line"><a name="l01462"></a><span class="lineno"> 1462</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01463"></a><span class="lineno"> 1463</span>  b_addr += <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>);</div><div class="line"><a name="l01464"></a><span class="lineno"> 1464</span> <span class="preprocessor">#endif // defined(HAS_BIAS)</span></div><div class="line"><a name="l01465"></a><span class="lineno"> 1465</span>  }</div><div class="line"><a name="l01466"></a><span class="lineno"> 1466</span> }</div><div class="line"><a name="l01467"></a><span class="lineno"> 1467</span> <span class="preprocessor">#endif // defined(SRC_DIM1) && defined(SRC_DIM2) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defiend(N0) && defined(DATA_TYPE) && defined(DILATION_X) && defined(DILATION_Y) && defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(CONV_PAD_LEFT) && defined(CONV_PAD_TOP)</span></div><div class="line"><a name="l01468"></a><span class="lineno"> 1468</span> </div><div class="line"><a name="l01469"></a><span class="lineno"> 1469</span> <span class="preprocessor">#if defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE)</span></div><div class="line"><a name="l01470"></a><span class="lineno"> 1470</span> </div><div class="line"><a name="l01471"></a><span class="lineno"> 1471</span> <span class="preprocessor">#if DATA_TYPE != float || DATA_TYPE != half</span></div><div class="line"><a name="l01472"></a><span class="lineno"> 1472</span> <span class="preprocessor">#error "Unsupported data type"</span></div><div class="line"><a name="l01473"></a><span class="lineno"> 1473</span> <span class="preprocessor">#endif // DATA_TYPE != float || DATA_TYPE != half</span></div><div class="line"><a name="l01474"></a><span class="lineno"> 1474</span> </div><div class="line"><a name="l01475"></a><span class="lineno"> 1475</span> <span class="preprocessor">#define VEC_FLOAT VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)</span></div><div class="line"><a name="l01476"></a><span class="lineno"> 1476</span> </div><div class="line"><a name="l01477"></a><span class="lineno"> 1477</span> <span class="preprocessor">#if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y)</span></div><div class="line"><a name="l01478"></a><span class="lineno"> 1478</span> <span class="comment"></span></div><div class="line"><a name="l01479"></a><span class="lineno"> 1479</span> <span class="comment">/** This function computes the depthwise convolution for NHWC data layout when the stride along the width or height is not 1.</span></div><div class="line"><a name="l01480"></a><span class="lineno"> 1480</span> <span class="comment"> *</span></div><div class="line"><a name="l01481"></a><span class="lineno"> 1481</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l01482"></a><span class="lineno"> 1482</span> <span class="comment"> * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2)</span></div><div class="line"><a name="l01483"></a><span class="lineno"> 1483</span> <span class="comment"> * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)</span></div><div class="line"><a name="l01484"></a><span class="lineno"> 1484</span> <span class="comment"> * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1)</span></div><div class="line"><a name="l01485"></a><span class="lineno"> 1485</span> <span class="comment"> * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1)</span></div><div class="line"><a name="l01486"></a><span class="lineno"> 1486</span> <span class="comment"> * @note The convolution stride along the width must be passed at compile time using -DCONV_STRIDE_X (e.g. -DCONV_STRIDE_Y=X)</span></div><div class="line"><a name="l01487"></a><span class="lineno"> 1487</span> <span class="comment"> * @note The convolution stride along the height must be passed at compile time using -DCONV_STRIDE_Y (e.g. -DCONV_STRIDE_Y=1)</span></div><div class="line"><a name="l01488"></a><span class="lineno"> 1488</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l01489"></a><span class="lineno"> 1489</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l01490"></a><span class="lineno"> 1490</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l01491"></a><span class="lineno"> 1491</span> <span class="comment"> *</span></div><div class="line"><a name="l01492"></a><span class="lineno"> 1492</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32</span></div><div class="line"><a name="l01493"></a><span class="lineno"> 1493</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01494"></a><span class="lineno"> 1494</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01495"></a><span class="lineno"> 1495</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01496"></a><span class="lineno"> 1496</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01497"></a><span class="lineno"> 1497</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01498"></a><span class="lineno"> 1498</span> <span class="comment"> * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l01499"></a><span class="lineno"> 1499</span> <span class="comment"> * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)</span></div><div class="line"><a name="l01500"></a><span class="lineno"> 1500</span> <span class="comment"> * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)</span></div><div class="line"><a name="l01501"></a><span class="lineno"> 1501</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l01502"></a><span class="lineno"> 1502</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr</span></div><div class="line"><a name="l01503"></a><span class="lineno"> 1503</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01504"></a><span class="lineno"> 1504</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01505"></a><span class="lineno"> 1505</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01506"></a><span class="lineno"> 1506</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01507"></a><span class="lineno"> 1507</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01508"></a><span class="lineno"> 1508</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01509"></a><span class="lineno"> 1509</span> <span class="comment"> * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes)</span></div><div class="line"><a name="l01510"></a><span class="lineno"> 1510</span> <span class="comment"> * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes)</span></div><div class="line"><a name="l01511"></a><span class="lineno"> 1511</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l01512"></a><span class="lineno"> 1512</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32</span></div><div class="line"><a name="l01513"></a><span class="lineno"> 1513</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01514"></a><span class="lineno"> 1514</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01515"></a><span class="lineno"> 1515</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01516"></a><span class="lineno"> 1516</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01517"></a><span class="lineno"> 1517</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01518"></a><span class="lineno"> 1518</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01519"></a><span class="lineno"> 1519</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor</span></div><div class="line"><a name="l01520"></a><span class="lineno"> 1520</span> <span class="comment"> * @param[in] max_offset Max offset for the input tensor</span></div><div class="line"><a name="l01521"></a><span class="lineno"> 1521</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr</span></div><div class="line"><a name="l01522"></a><span class="lineno"> 1522</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l01523"></a><span class="lineno"> 1523</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01524"></a><span class="lineno"> 1524</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l01525"></a><span class="lineno"> 1525</span> <span class="comment"> */</span></div><div class="line"><a name="l01526"></a><span class="lineno"> 1526</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_nhwc(</div><div class="line"><a name="l01527"></a><span class="lineno"> 1527</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l01528"></a><span class="lineno"> 1528</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l01529"></a><span class="lineno"> 1529</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>),</div><div class="line"><a name="l01530"></a><span class="lineno"> 1530</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l01531"></a><span class="lineno"> 1531</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases),</div><div class="line"><a name="l01532"></a><span class="lineno"> 1532</span> #endif <span class="comment">/* defined(HAS_BIAS) */</span></div><div class="line"><a name="l01533"></a><span class="lineno"> 1533</span>  <span class="keywordtype">int</span> max_offset)</div><div class="line"><a name="l01534"></a><span class="lineno"> 1534</span> {</div><div class="line"><a name="l01535"></a><span class="lineno"> 1535</span>  <span class="keywordtype">int</span> x = get_global_id(0); <span class="comment">// channels</span></div><div class="line"><a name="l01536"></a><span class="lineno"> 1536</span>  <span class="keywordtype">int</span> y = get_global_id(1); <span class="comment">// spatial coordinate x</span></div><div class="line"><a name="l01537"></a><span class="lineno"> 1537</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01538"></a><span class="lineno"> 1538</span>  <span class="keywordtype">int</span> z = get_global_id(2) % (int)DST_DEPTH; <span class="comment">// spatial coordinate y</span></div><div class="line"><a name="l01539"></a><span class="lineno"> 1539</span>  <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> = get_global_id(2) / (int)DST_DEPTH; <span class="comment">// batch</span></div><div class="line"><a name="l01540"></a><span class="lineno"> 1540</span> <span class="preprocessor">#else // defined(DST_DEPTH)</span></div><div class="line"><a name="l01541"></a><span class="lineno"> 1541</span>  <span class="keywordtype">int</span> z = get_global_id(2); <span class="comment">// spatial coordinate y</span></div><div class="line"><a name="l01542"></a><span class="lineno"> 1542</span> <span class="preprocessor">#endif // defined(DST_DEPTH)</span></div><div class="line"><a name="l01543"></a><span class="lineno"> 1543</span> </div><div class="line"><a name="l01544"></a><span class="lineno"> 1544</span>  <a class="code" href="struct_vector.xhtml">Vector</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a527bfdf5eeb306f1cf01c4a8e29f38e0">CONVERT_TO_VECTOR_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l01545"></a><span class="lineno"> 1545</span> </div><div class="line"><a name="l01546"></a><span class="lineno"> 1546</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01547"></a><span class="lineno"> 1547</span>  __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a> + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * src_stride_w;</div><div class="line"><a name="l01548"></a><span class="lineno"> 1548</span> <span class="preprocessor">#else </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01549"></a><span class="lineno"> 1549</span>  __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>;</div><div class="line"><a name="l01550"></a><span class="lineno"> 1550</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01551"></a><span class="lineno"> 1551</span> </div><div class="line"><a name="l01552"></a><span class="lineno"> 1552</span>  <span class="keywordtype">int</span> z_coord = 0;</div><div class="line"><a name="l01553"></a><span class="lineno"> 1553</span>  int4 <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0;</div><div class="line"><a name="l01554"></a><span class="lineno"> 1554</span>  int4 y_offset = ((int4)(y * CONV_STRIDE_X) + (int4)(0, DILATION_X * 1, DILATION_X * 2, DILATION_X * 3) - CONV_PAD_LEFT) * (int4)src_stride_y;</div><div class="line"><a name="l01555"></a><span class="lineno"> 1555</span> </div><div class="line"><a name="l01556"></a><span class="lineno"> 1556</span>  <span class="comment">// We compute 2x1x1 [C,W,H] elements</span></div><div class="line"><a name="l01557"></a><span class="lineno"> 1557</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> acc = 0;</div><div class="line"><a name="l01558"></a><span class="lineno"> 1558</span> </div><div class="line"><a name="l01559"></a><span class="lineno"> 1559</span>  <span class="comment">// Load weights</span></div><div class="line"><a name="l01560"></a><span class="lineno"> 1560</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 0 * weights_stride_y + 0 * weights_stride_z));</div><div class="line"><a name="l01561"></a><span class="lineno"> 1561</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 1 * weights_stride_y + 0 * weights_stride_z));</div><div class="line"><a name="l01562"></a><span class="lineno"> 1562</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 2 * weights_stride_y + 0 * weights_stride_z));</div><div class="line"><a name="l01563"></a><span class="lineno"> 1563</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w3 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 0 * weights_stride_y + 1 * weights_stride_z));</div><div class="line"><a name="l01564"></a><span class="lineno"> 1564</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w4 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 1 * weights_stride_y + 1 * weights_stride_z));</div><div class="line"><a name="l01565"></a><span class="lineno"> 1565</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w5 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 2 * weights_stride_y + 1 * weights_stride_z));</div><div class="line"><a name="l01566"></a><span class="lineno"> 1566</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w6 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 0 * weights_stride_y + 2 * weights_stride_z));</div><div class="line"><a name="l01567"></a><span class="lineno"> 1567</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w7 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 1 * weights_stride_y + 2 * weights_stride_z));</div><div class="line"><a name="l01568"></a><span class="lineno"> 1568</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w8 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 2 * weights_stride_y + 2 * weights_stride_z));</div><div class="line"><a name="l01569"></a><span class="lineno"> 1569</span> </div><div class="line"><a name="l01570"></a><span class="lineno"> 1570</span>  <span class="comment">// Load input values</span></div><div class="line"><a name="l01571"></a><span class="lineno"> 1571</span>  <span class="comment">// z == 0</span></div><div class="line"><a name="l01572"></a><span class="lineno"> 1572</span>  <span class="comment">// Clamp z_coord as for z = 0, it can be negative</span></div><div class="line"><a name="l01573"></a><span class="lineno"> 1573</span>  <span class="comment">// z_coord is casted to unsigned int in order to use just a min() operation</span></div><div class="line"><a name="l01574"></a><span class="lineno"> 1574</span>  <span class="comment">// A "-1" 32 bit signed variable converted to unsigned gives 4294967295</span></div><div class="line"><a name="l01575"></a><span class="lineno"> 1575</span>  z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP;</div><div class="line"><a name="l01576"></a><span class="lineno"> 1576</span>  z_coord = min((uint)z_coord, (uint)SRC_DIM_2);</div><div class="line"><a name="l01577"></a><span class="lineno"> 1577</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = y_offset + (int4)(z_coord * src_stride_z);</div><div class="line"><a name="l01578"></a><span class="lineno"> 1578</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = min(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, (int4)max_offset);</div><div class="line"><a name="l01579"></a><span class="lineno"> 1579</span> </div><div class="line"><a name="l01580"></a><span class="lineno"> 1580</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01581"></a><span class="lineno"> 1581</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01582"></a><span class="lineno"> 1582</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01583"></a><span class="lineno"> 1583</span> </div><div class="line"><a name="l01584"></a><span class="lineno"> 1584</span>  <span class="comment">// z == 1</span></div><div class="line"><a name="l01585"></a><span class="lineno"> 1585</span>  <span class="comment">// z_coord can be only negative for z = 0 so we do not need to clamp it</span></div><div class="line"><a name="l01586"></a><span class="lineno"> 1586</span>  <span class="comment">// Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset</span></div><div class="line"><a name="l01587"></a><span class="lineno"> 1587</span>  z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y;</div><div class="line"><a name="l01588"></a><span class="lineno"> 1588</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = y_offset + (int4)(z_coord * src_stride_z);</div><div class="line"><a name="l01589"></a><span class="lineno"> 1589</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values3 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01590"></a><span class="lineno"> 1590</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values4 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01591"></a><span class="lineno"> 1591</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values5 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01592"></a><span class="lineno"> 1592</span> </div><div class="line"><a name="l01593"></a><span class="lineno"> 1593</span>  <span class="comment">// z == 2</span></div><div class="line"><a name="l01594"></a><span class="lineno"> 1594</span>  <span class="comment">// Offset can be out-of-bound so we need to check if it is greater than max_offset</span></div><div class="line"><a name="l01595"></a><span class="lineno"> 1595</span>  z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y * 2;</div><div class="line"><a name="l01596"></a><span class="lineno"> 1596</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = y_offset + (int4)(z_coord * src_stride_z);</div><div class="line"><a name="l01597"></a><span class="lineno"> 1597</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = min(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, (int4)max_offset);</div><div class="line"><a name="l01598"></a><span class="lineno"> 1598</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values6 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01599"></a><span class="lineno"> 1599</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values7 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01600"></a><span class="lineno"> 1600</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values8 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01601"></a><span class="lineno"> 1601</span> </div><div class="line"><a name="l01602"></a><span class="lineno"> 1602</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values0, w0, acc);</div><div class="line"><a name="l01603"></a><span class="lineno"> 1603</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values1, w1, acc);</div><div class="line"><a name="l01604"></a><span class="lineno"> 1604</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values2, w2, acc);</div><div class="line"><a name="l01605"></a><span class="lineno"> 1605</span> </div><div class="line"><a name="l01606"></a><span class="lineno"> 1606</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values3, w3, acc);</div><div class="line"><a name="l01607"></a><span class="lineno"> 1607</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values4, w4, acc);</div><div class="line"><a name="l01608"></a><span class="lineno"> 1608</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values5, w5, acc);</div><div class="line"><a name="l01609"></a><span class="lineno"> 1609</span> </div><div class="line"><a name="l01610"></a><span class="lineno"> 1610</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values6, w6, acc);</div><div class="line"><a name="l01611"></a><span class="lineno"> 1611</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values7, w7, acc);</div><div class="line"><a name="l01612"></a><span class="lineno"> 1612</span>  acc = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values8, w8, acc);</div><div class="line"><a name="l01613"></a><span class="lineno"> 1613</span> </div><div class="line"><a name="l01614"></a><span class="lineno"> 1614</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01615"></a><span class="lineno"> 1615</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a527bfdf5eeb306f1cf01c4a8e29f38e0">CONVERT_TO_VECTOR_STRUCT</a>(biases);</div><div class="line"><a name="l01616"></a><span class="lineno"> 1616</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> bias_values = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)biases.<a class="code" href="struct_vector.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>);</div><div class="line"><a name="l01617"></a><span class="lineno"> 1617</span>  acc += bias_values;</div><div class="line"><a name="l01618"></a><span class="lineno"> 1618</span> <span class="preprocessor">#endif // defined(HAS_BIAS)</span></div><div class="line"><a name="l01619"></a><span class="lineno"> 1619</span> </div><div class="line"><a name="l01620"></a><span class="lineno"> 1620</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01621"></a><span class="lineno"> 1621</span>  __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + z * dst_step_z + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * dst_stride_w;</div><div class="line"><a name="l01622"></a><span class="lineno"> 1622</span> <span class="preprocessor">#else </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01623"></a><span class="lineno"> 1623</span>  __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + z * dst_step_z;</div><div class="line"><a name="l01624"></a><span class="lineno"> 1624</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01625"></a><span class="lineno"> 1625</span> </div><div class="line"><a name="l01626"></a><span class="lineno"> 1626</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l01627"></a><span class="lineno"> 1627</span>  (<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, acc, A_VAL, B_VAL), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_addr));</div><div class="line"><a name="l01628"></a><span class="lineno"> 1628</span> }</div><div class="line"><a name="l01629"></a><span class="lineno"> 1629</span> <span class="preprocessor">#endif // defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y)</span></div><div class="line"><a name="l01630"></a><span class="lineno"> 1630</span> </div><div class="line"><a name="l01631"></a><span class="lineno"> 1631</span> <span class="preprocessor">#if defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED)</span></div><div class="line"><a name="l01632"></a><span class="lineno"> 1632</span> <span class="comment">/** This function computes the depthwise convolution for NHWC data layout when the stride along the width and height is 1.</span></div><div class="line"><a name="l01633"></a><span class="lineno"> 1633</span> <span class="comment"> *</span></div><div class="line"><a name="l01634"></a><span class="lineno"> 1634</span> <span class="comment"> * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l01635"></a><span class="lineno"> 1635</span> <span class="comment"> * @note The number of elements read per thread must be passed at compile time using -DVEC_SIZE (e.g. -DVEC_SIZE=2)</span></div><div class="line"><a name="l01636"></a><span class="lineno"> 1636</span> <span class="comment"> * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)</span></div><div class="line"><a name="l01637"></a><span class="lineno"> 1637</span> <span class="comment"> * @note The number of rows processed per thread must be passed at compile time using -DNUM_ROWS_PROCESSED (i.e. -DNUM_ROWS_PROCESSED=2)</span></div><div class="line"><a name="l01638"></a><span class="lineno"> 1638</span> <span class="comment"> * @note The number of planes processed per thread must be passed at compile time using -DNUM_PLANES_PROCESSED (i.e. -DNUM_PLANES_PROCESSED=2)</span></div><div class="line"><a name="l01639"></a><span class="lineno"> 1639</span> <span class="comment"> * @note The convolution pad top must be passed at compile time using -DCONV_PAD_TOP (e.g. -DCONV_PAD_TOP=1)</span></div><div class="line"><a name="l01640"></a><span class="lineno"> 1640</span> <span class="comment"> * @note The convolution pad top must be passed at compile time using -DCONV_PAD_LEFT (e.g. -DCONV_PAD_LEFT=1)</span></div><div class="line"><a name="l01641"></a><span class="lineno"> 1641</span> <span class="comment"> * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu</span></div><div class="line"><a name="l01642"></a><span class="lineno"> 1642</span> <span class="comment"> * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively</span></div><div class="line"><a name="l01643"></a><span class="lineno"> 1643</span> <span class="comment"> * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size</span></div><div class="line"><a name="l01644"></a><span class="lineno"> 1644</span> <span class="comment"> *</span></div><div class="line"><a name="l01645"></a><span class="lineno"> 1645</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32</span></div><div class="line"><a name="l01646"></a><span class="lineno"> 1646</span> <span class="comment"> * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01647"></a><span class="lineno"> 1647</span> <span class="comment"> * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01648"></a><span class="lineno"> 1648</span> <span class="comment"> * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01649"></a><span class="lineno"> 1649</span> <span class="comment"> * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01650"></a><span class="lineno"> 1650</span> <span class="comment"> * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01651"></a><span class="lineno"> 1651</span> <span class="comment"> * @param[in] src_step_z src_stride_y * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l01652"></a><span class="lineno"> 1652</span> <span class="comment"> * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes)</span></div><div class="line"><a name="l01653"></a><span class="lineno"> 1653</span> <span class="comment"> * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes)</span></div><div class="line"><a name="l01654"></a><span class="lineno"> 1654</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l01655"></a><span class="lineno"> 1655</span> <span class="comment"> * @param[in] dst_ptr Pointer to the destination tensor. Supported data types: same as src_ptr</span></div><div class="line"><a name="l01656"></a><span class="lineno"> 1656</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01657"></a><span class="lineno"> 1657</span> <span class="comment"> * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01658"></a><span class="lineno"> 1658</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01659"></a><span class="lineno"> 1659</span> <span class="comment"> * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01660"></a><span class="lineno"> 1660</span> <span class="comment"> * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01661"></a><span class="lineno"> 1661</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01662"></a><span class="lineno"> 1662</span> <span class="comment"> * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes)</span></div><div class="line"><a name="l01663"></a><span class="lineno"> 1663</span> <span class="comment"> * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes)</span></div><div class="line"><a name="l01664"></a><span class="lineno"> 1664</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor</span></div><div class="line"><a name="l01665"></a><span class="lineno"> 1665</span> <span class="comment"> * @param[in] weights_ptr Pointer to the weights tensor. Supported data types: F16/F32</span></div><div class="line"><a name="l01666"></a><span class="lineno"> 1666</span> <span class="comment"> * @param[in] weights_stride_x Stride of the weights tensor in X dimension (in bytes)</span></div><div class="line"><a name="l01667"></a><span class="lineno"> 1667</span> <span class="comment"> * @param[in] weights_step_x weights_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01668"></a><span class="lineno"> 1668</span> <span class="comment"> * @param[in] weights_stride_y Stride of the weights tensor in Y dimension (in bytes)</span></div><div class="line"><a name="l01669"></a><span class="lineno"> 1669</span> <span class="comment"> * @param[in] weights_step_y weights_stride_y * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01670"></a><span class="lineno"> 1670</span> <span class="comment"> * @param[in] weights_stride_z Stride of the weights tensor in Z dimension (in bytes)</span></div><div class="line"><a name="l01671"></a><span class="lineno"> 1671</span> <span class="comment"> * @param[in] weights_step_z weights_stride_z * number of elements along Y processed per workitem(in bytes)</span></div><div class="line"><a name="l01672"></a><span class="lineno"> 1672</span> <span class="comment"> * @param[in] weights_offset_first_element_in_bytes The offset of the first element in the weights tensor</span></div><div class="line"><a name="l01673"></a><span class="lineno"> 1673</span> <span class="comment"> * @param[in] max_offset Max offset for the input tensor</span></div><div class="line"><a name="l01674"></a><span class="lineno"> 1674</span> <span class="comment"> * @param[in] biases_ptr (Optional) Pointer to the biases vector. Supported data types: same as src_ptr</span></div><div class="line"><a name="l01675"></a><span class="lineno"> 1675</span> <span class="comment"> * @param[in] biases_stride_x (Optional) Stride of the biases vector in X dimension (in bytes)</span></div><div class="line"><a name="l01676"></a><span class="lineno"> 1676</span> <span class="comment"> * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)</span></div><div class="line"><a name="l01677"></a><span class="lineno"> 1677</span> <span class="comment"> * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases vector</span></div><div class="line"><a name="l01678"></a><span class="lineno"> 1678</span> <span class="comment"> */</span></div><div class="line"><a name="l01679"></a><span class="lineno"> 1679</span> __kernel <span class="keywordtype">void</span> depthwise_convolution_3x3_nhwc_stride1(</div><div class="line"><a name="l01680"></a><span class="lineno"> 1680</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l01681"></a><span class="lineno"> 1681</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a481bdc6d61b3df9dcdbdb244f0f97790">TENSOR4D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>),</div><div class="line"><a name="l01682"></a><span class="lineno"> 1682</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>),</div><div class="line"><a name="l01683"></a><span class="lineno"> 1683</span> #<span class="keywordflow">if</span> defined(HAS_BIAS)</div><div class="line"><a name="l01684"></a><span class="lineno"> 1684</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a40a6eb9f2a7712f08d6bb8ff6c9e6ca7">VECTOR_DECLARATION</a>(biases),</div><div class="line"><a name="l01685"></a><span class="lineno"> 1685</span> #endif <span class="comment">/* defined(HAS_BIAS) */</span></div><div class="line"><a name="l01686"></a><span class="lineno"> 1686</span>  <span class="keywordtype">int</span> max_offset)</div><div class="line"><a name="l01687"></a><span class="lineno"> 1687</span> {</div><div class="line"><a name="l01688"></a><span class="lineno"> 1688</span>  <span class="keywordtype">int</span> x = get_global_id(0); <span class="comment">// channels</span></div><div class="line"><a name="l01689"></a><span class="lineno"> 1689</span>  <span class="keywordtype">int</span> y = get_global_id(1); <span class="comment">// spatial coordinate x</span></div><div class="line"><a name="l01690"></a><span class="lineno"> 1690</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01691"></a><span class="lineno"> 1691</span>  <span class="keywordtype">int</span> z = get_global_id(2) % (int)DST_DEPTH; <span class="comment">// spatial coordinate y</span></div><div class="line"><a name="l01692"></a><span class="lineno"> 1692</span>  <span class="keywordtype">int</span> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> = get_global_id(2) / (int)DST_DEPTH; <span class="comment">// batch</span></div><div class="line"><a name="l01693"></a><span class="lineno"> 1693</span> <span class="preprocessor">#else // defined(DST_DEPTH)</span></div><div class="line"><a name="l01694"></a><span class="lineno"> 1694</span>  <span class="keywordtype">int</span> z = get_global_id(2); <span class="comment">// spatial coordinate y</span></div><div class="line"><a name="l01695"></a><span class="lineno"> 1695</span> <span class="preprocessor">#endif // defined(DST_DEPTH)</span></div><div class="line"><a name="l01696"></a><span class="lineno"> 1696</span> </div><div class="line"><a name="l01697"></a><span class="lineno"> 1697</span>  <a class="code" href="struct_vector.xhtml">Vector</a> <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a> = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a527bfdf5eeb306f1cf01c4a8e29f38e0">CONVERT_TO_VECTOR_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>);</div><div class="line"><a name="l01698"></a><span class="lineno"> 1698</span> </div><div class="line"><a name="l01699"></a><span class="lineno"> 1699</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01700"></a><span class="lineno"> 1700</span>  __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a> + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * src_stride_w;</div><div class="line"><a name="l01701"></a><span class="lineno"> 1701</span> <span class="preprocessor">#else </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01702"></a><span class="lineno"> 1702</span>  __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) * <a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>;</div><div class="line"><a name="l01703"></a><span class="lineno"> 1703</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01704"></a><span class="lineno"> 1704</span> </div><div class="line"><a name="l01705"></a><span class="lineno"> 1705</span>  <span class="keywordtype">int</span> z_coord = 0;</div><div class="line"><a name="l01706"></a><span class="lineno"> 1706</span>  int4 <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = 0;</div><div class="line"><a name="l01707"></a><span class="lineno"> 1707</span>  int4 y_offset = ((int4)(y * NUM_ROWS_PROCESSED) + (int4)(0, 1, 2, 3) - (int)CONV_PAD_LEFT) * (int4)src_stride_y;</div><div class="line"><a name="l01708"></a><span class="lineno"> 1708</span> </div><div class="line"><a name="l01709"></a><span class="lineno"> 1709</span>  <span class="comment">// We compute 2x2x2 [C,W,H] elements</span></div><div class="line"><a name="l01710"></a><span class="lineno"> 1710</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> acc0 = 0;</div><div class="line"><a name="l01711"></a><span class="lineno"> 1711</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> acc1 = 0;</div><div class="line"><a name="l01712"></a><span class="lineno"> 1712</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> acc2 = 0;</div><div class="line"><a name="l01713"></a><span class="lineno"> 1713</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> acc3 = 0;</div><div class="line"><a name="l01714"></a><span class="lineno"> 1714</span> </div><div class="line"><a name="l01715"></a><span class="lineno"> 1715</span>  <span class="comment">// Load weights</span></div><div class="line"><a name="l01716"></a><span class="lineno"> 1716</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 0 * weights_stride_y + 0 * weights_stride_z));</div><div class="line"><a name="l01717"></a><span class="lineno"> 1717</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 1 * weights_stride_y + 0 * weights_stride_z));</div><div class="line"><a name="l01718"></a><span class="lineno"> 1718</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 2 * weights_stride_y + 0 * weights_stride_z));</div><div class="line"><a name="l01719"></a><span class="lineno"> 1719</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w3 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 0 * weights_stride_y + 1 * weights_stride_z));</div><div class="line"><a name="l01720"></a><span class="lineno"> 1720</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w4 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 1 * weights_stride_y + 1 * weights_stride_z));</div><div class="line"><a name="l01721"></a><span class="lineno"> 1721</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w5 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 2 * weights_stride_y + 1 * weights_stride_z));</div><div class="line"><a name="l01722"></a><span class="lineno"> 1722</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w6 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 0 * weights_stride_y + 2 * weights_stride_z));</div><div class="line"><a name="l01723"></a><span class="lineno"> 1723</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w7 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 1 * weights_stride_y + 2 * weights_stride_z));</div><div class="line"><a name="l01724"></a><span class="lineno"> 1724</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> w8 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a64a08a9fec5aeee8650e7182b6d171d0">weights</a>.ptr + 2 * weights_stride_y + 2 * weights_stride_z));</div><div class="line"><a name="l01725"></a><span class="lineno"> 1725</span> </div><div class="line"><a name="l01726"></a><span class="lineno"> 1726</span>  <span class="comment">// Load input values</span></div><div class="line"><a name="l01727"></a><span class="lineno"> 1727</span>  <span class="comment">// z == 0</span></div><div class="line"><a name="l01728"></a><span class="lineno"> 1728</span>  <span class="comment">// Clamp z_coord as for z = 0, it can be negative</span></div><div class="line"><a name="l01729"></a><span class="lineno"> 1729</span>  <span class="comment">// z_coord is casted to unsigned int in order to use just a min() operation</span></div><div class="line"><a name="l01730"></a><span class="lineno"> 1730</span>  <span class="comment">// A "-1" 32 bit signed variable converted to unsigned gives 4294967295</span></div><div class="line"><a name="l01731"></a><span class="lineno"> 1731</span>  z_coord = z * (int)NUM_PLANES_PROCESSED - (<span class="keywordtype">int</span>)CONV_PAD_TOP;</div><div class="line"><a name="l01732"></a><span class="lineno"> 1732</span>  z_coord = min((uint)z_coord, (uint)SRC_DIM_2);</div><div class="line"><a name="l01733"></a><span class="lineno"> 1733</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = y_offset + (int4)(z_coord * src_stride_z);</div><div class="line"><a name="l01734"></a><span class="lineno"> 1734</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = min(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, (int4)max_offset);</div><div class="line"><a name="l01735"></a><span class="lineno"> 1735</span> </div><div class="line"><a name="l01736"></a><span class="lineno"> 1736</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01737"></a><span class="lineno"> 1737</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01738"></a><span class="lineno"> 1738</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01739"></a><span class="lineno"> 1739</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values3 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s3));</div><div class="line"><a name="l01740"></a><span class="lineno"> 1740</span> </div><div class="line"><a name="l01741"></a><span class="lineno"> 1741</span>  <span class="comment">// z == 1</span></div><div class="line"><a name="l01742"></a><span class="lineno"> 1742</span>  <span class="comment">// z_coord can be only negative for z = 0 so we do not need to clamp it</span></div><div class="line"><a name="l01743"></a><span class="lineno"> 1743</span>  <span class="comment">// Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset</span></div><div class="line"><a name="l01744"></a><span class="lineno"> 1744</span>  z_coord = z * (int)NUM_PLANES_PROCESSED - (<span class="keywordtype">int</span>)CONV_PAD_TOP + 1;</div><div class="line"><a name="l01745"></a><span class="lineno"> 1745</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = y_offset + (int4)(z_coord * src_stride_z);</div><div class="line"><a name="l01746"></a><span class="lineno"> 1746</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values4 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01747"></a><span class="lineno"> 1747</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values5 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01748"></a><span class="lineno"> 1748</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values6 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01749"></a><span class="lineno"> 1749</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values7 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s3));</div><div class="line"><a name="l01750"></a><span class="lineno"> 1750</span> </div><div class="line"><a name="l01751"></a><span class="lineno"> 1751</span>  <span class="comment">// z == 2</span></div><div class="line"><a name="l01752"></a><span class="lineno"> 1752</span>  <span class="comment">// After z = 1 we can simply add src_stride_z to offset without updating z_coord</span></div><div class="line"><a name="l01753"></a><span class="lineno"> 1753</span>  <span class="comment">// However offset can be out-of-bound so we need to check if it is greater than max_offset</span></div><div class="line"><a name="l01754"></a><span class="lineno"> 1754</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += (int4)src_stride_z;</div><div class="line"><a name="l01755"></a><span class="lineno"> 1755</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = min(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, (int4)max_offset);</div><div class="line"><a name="l01756"></a><span class="lineno"> 1756</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values8 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01757"></a><span class="lineno"> 1757</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values9 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01758"></a><span class="lineno"> 1758</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values10 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01759"></a><span class="lineno"> 1759</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values11 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s3));</div><div class="line"><a name="l01760"></a><span class="lineno"> 1760</span> </div><div class="line"><a name="l01761"></a><span class="lineno"> 1761</span>  <span class="comment">// z == 3</span></div><div class="line"><a name="l01762"></a><span class="lineno"> 1762</span>  <span class="comment">// After z = 1 we can simply add src_stride_z to offset without updating z_coord</span></div><div class="line"><a name="l01763"></a><span class="lineno"> 1763</span>  <span class="comment">// However offset can be out-of-bound so we need to check if it is greater than max_offset</span></div><div class="line"><a name="l01764"></a><span class="lineno"> 1764</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> += (int4)src_stride_z;</div><div class="line"><a name="l01765"></a><span class="lineno"> 1765</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = min(<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>, (int4)max_offset);</div><div class="line"><a name="l01766"></a><span class="lineno"> 1766</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values12 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s0));</div><div class="line"><a name="l01767"></a><span class="lineno"> 1767</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values13 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s1));</div><div class="line"><a name="l01768"></a><span class="lineno"> 1768</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values14 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s2));</div><div class="line"><a name="l01769"></a><span class="lineno"> 1769</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> values15 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(src_addr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>.s3));</div><div class="line"><a name="l01770"></a><span class="lineno"> 1770</span> </div><div class="line"><a name="l01771"></a><span class="lineno"> 1771</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values0, w0, acc0);</div><div class="line"><a name="l01772"></a><span class="lineno"> 1772</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values1, w1, acc0);</div><div class="line"><a name="l01773"></a><span class="lineno"> 1773</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values2, w2, acc0);</div><div class="line"><a name="l01774"></a><span class="lineno"> 1774</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values1, w0, acc1);</div><div class="line"><a name="l01775"></a><span class="lineno"> 1775</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values2, w1, acc1);</div><div class="line"><a name="l01776"></a><span class="lineno"> 1776</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values3, w2, acc1);</div><div class="line"><a name="l01777"></a><span class="lineno"> 1777</span> </div><div class="line"><a name="l01778"></a><span class="lineno"> 1778</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values4, w3, acc0);</div><div class="line"><a name="l01779"></a><span class="lineno"> 1779</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values5, w4, acc0);</div><div class="line"><a name="l01780"></a><span class="lineno"> 1780</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values6, w5, acc0);</div><div class="line"><a name="l01781"></a><span class="lineno"> 1781</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values5, w3, acc1);</div><div class="line"><a name="l01782"></a><span class="lineno"> 1782</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values6, w4, acc1);</div><div class="line"><a name="l01783"></a><span class="lineno"> 1783</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values7, w5, acc1);</div><div class="line"><a name="l01784"></a><span class="lineno"> 1784</span> </div><div class="line"><a name="l01785"></a><span class="lineno"> 1785</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values8, w6, acc0);</div><div class="line"><a name="l01786"></a><span class="lineno"> 1786</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values9, w7, acc0);</div><div class="line"><a name="l01787"></a><span class="lineno"> 1787</span>  acc0 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values10, w8, acc0);</div><div class="line"><a name="l01788"></a><span class="lineno"> 1788</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values9, w6, acc1);</div><div class="line"><a name="l01789"></a><span class="lineno"> 1789</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values10, w7, acc1);</div><div class="line"><a name="l01790"></a><span class="lineno"> 1790</span>  acc1 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values11, w8, acc1);</div><div class="line"><a name="l01791"></a><span class="lineno"> 1791</span> </div><div class="line"><a name="l01792"></a><span class="lineno"> 1792</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values4, w0, acc2);</div><div class="line"><a name="l01793"></a><span class="lineno"> 1793</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values5, w1, acc2);</div><div class="line"><a name="l01794"></a><span class="lineno"> 1794</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values6, w2, acc2);</div><div class="line"><a name="l01795"></a><span class="lineno"> 1795</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values5, w0, acc3);</div><div class="line"><a name="l01796"></a><span class="lineno"> 1796</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values6, w1, acc3);</div><div class="line"><a name="l01797"></a><span class="lineno"> 1797</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values7, w2, acc3);</div><div class="line"><a name="l01798"></a><span class="lineno"> 1798</span> </div><div class="line"><a name="l01799"></a><span class="lineno"> 1799</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values8, w3, acc2);</div><div class="line"><a name="l01800"></a><span class="lineno"> 1800</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values9, w4, acc2);</div><div class="line"><a name="l01801"></a><span class="lineno"> 1801</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values10, w5, acc2);</div><div class="line"><a name="l01802"></a><span class="lineno"> 1802</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values9, w3, acc3);</div><div class="line"><a name="l01803"></a><span class="lineno"> 1803</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values10, w4, acc3);</div><div class="line"><a name="l01804"></a><span class="lineno"> 1804</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values11, w5, acc3);</div><div class="line"><a name="l01805"></a><span class="lineno"> 1805</span> </div><div class="line"><a name="l01806"></a><span class="lineno"> 1806</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values12, w6, acc2);</div><div class="line"><a name="l01807"></a><span class="lineno"> 1807</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values13, w7, acc2);</div><div class="line"><a name="l01808"></a><span class="lineno"> 1808</span>  acc2 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values14, w8, acc2);</div><div class="line"><a name="l01809"></a><span class="lineno"> 1809</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values13, w6, acc3);</div><div class="line"><a name="l01810"></a><span class="lineno"> 1810</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values14, w7, acc3);</div><div class="line"><a name="l01811"></a><span class="lineno"> 1811</span>  acc3 = <a class="code" href="namespacearm__compute_1_1support_1_1cpp11.xhtml#af399bedeaeb8dc177d3a301a12c3a5d0">fma</a>(values15, w8, acc3);</div><div class="line"><a name="l01812"></a><span class="lineno"> 1812</span> </div><div class="line"><a name="l01813"></a><span class="lineno"> 1813</span> <span class="preprocessor">#if defined(HAS_BIAS)</span></div><div class="line"><a name="l01814"></a><span class="lineno"> 1814</span>  <a class="code" href="struct_vector.xhtml">Vector</a> biases = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a527bfdf5eeb306f1cf01c4a8e29f38e0">CONVERT_TO_VECTOR_STRUCT</a>(biases);</div><div class="line"><a name="l01815"></a><span class="lineno"> 1815</span> </div><div class="line"><a name="l01816"></a><span class="lineno"> 1816</span>  <a class="code" href="activation__layer__quant_8cl.xhtml#ade2e33e6f303ce93468eef7e56d95c0c">VEC_FLOAT</a> bias_values = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)biases.<a class="code" href="struct_vector.xhtml#acf52c23cbd7424606c10a606524e3e32">ptr</a>);</div><div class="line"><a name="l01817"></a><span class="lineno"> 1817</span> </div><div class="line"><a name="l01818"></a><span class="lineno"> 1818</span>  acc0 += bias_values;</div><div class="line"><a name="l01819"></a><span class="lineno"> 1819</span>  acc1 += bias_values;</div><div class="line"><a name="l01820"></a><span class="lineno"> 1820</span>  acc2 += bias_values;</div><div class="line"><a name="l01821"></a><span class="lineno"> 1821</span>  acc3 += bias_values;</div><div class="line"><a name="l01822"></a><span class="lineno"> 1822</span> <span class="preprocessor">#endif // defined(HAS_BIAS)</span></div><div class="line"><a name="l01823"></a><span class="lineno"> 1823</span> </div><div class="line"><a name="l01824"></a><span class="lineno"> 1824</span> <span class="preprocessor">#if defined(DST_DEPTH)</span></div><div class="line"><a name="l01825"></a><span class="lineno"> 1825</span>  __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + (z * NUM_PLANES_PROCESSED) * dst_step_z + <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#aa76b4a6e74940dabc5b7fc6b2dab3545">b</a> * dst_stride_w;</div><div class="line"><a name="l01826"></a><span class="lineno"> 1826</span> <span class="preprocessor">#else </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01827"></a><span class="lineno"> 1827</span>  __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x * dst_step_x + y * dst_step_y + (z * NUM_PLANES_PROCESSED) * dst_step_z;</div><div class="line"><a name="l01828"></a><span class="lineno"> 1828</span> <span class="preprocessor">#endif </span><span class="comment">/* defined(DST_DEPTH) */</span><span class="preprocessor"></span></div><div class="line"><a name="l01829"></a><span class="lineno"> 1829</span> </div><div class="line"><a name="l01830"></a><span class="lineno"> 1830</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l01831"></a><span class="lineno"> 1831</span>  (<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, acc0, A_VAL, B_VAL), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_addr + 0 * dst_stride_y));</div><div class="line"><a name="l01832"></a><span class="lineno"> 1832</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l01833"></a><span class="lineno"> 1833</span>  (<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, acc1, A_VAL, B_VAL), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_addr + 1 * dst_stride_y));</div><div class="line"><a name="l01834"></a><span class="lineno"> 1834</span> </div><div class="line"><a name="l01835"></a><span class="lineno"> 1835</span> <span class="preprocessor">#if((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0)</span></div><div class="line"><a name="l01836"></a><span class="lineno"> 1836</span>  <span class="keywordflow">if</span>((z * NUM_PLANES_PROCESSED + 1) < DST_DIM_2)</div><div class="line"><a name="l01837"></a><span class="lineno"> 1837</span> #endif <span class="comment">// ((DST_DIM_2 % NUM_PLANES_PROCESSED) != 0)</span></div><div class="line"><a name="l01838"></a><span class="lineno"> 1838</span>  {</div><div class="line"><a name="l01839"></a><span class="lineno"> 1839</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l01840"></a><span class="lineno"> 1840</span>  (<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, acc2, A_VAL, B_VAL), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_addr + 0 * dst_stride_y + 1 * dst_stride_z));</div><div class="line"><a name="l01841"></a><span class="lineno"> 1841</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="depthwise__convolution__quantized_8cl.xhtml#a3fffea119c04c7680f2e9cf3fadf63b4">VEC_SIZE</a>)</div><div class="line"><a name="l01842"></a><span class="lineno"> 1842</span>  (<a class="code" href="activation__float__helpers_8h.xhtml#abbc420da5dec17216bb014c05ad65304">ACTIVATION</a>(ACTIVATION_TYPE, <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, acc3, A_VAL, B_VAL), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_addr + 1 * dst_stride_y + 1 * dst_stride_z));</div><div class="line"><a name="l01843"></a><span class="lineno"> 1843</span>  }</div><div class="line"><a name="l01844"></a><span class="lineno"> 1844</span> }</div><div class="line"><a name="l01845"></a><span class="lineno"> 1845</span> </div><div class="line"><a name="l01846"></a><span class="lineno"> 1846</span> <span class="preprocessor">#endif // defined(NUM_ROWS_PROCESSED) && defined(NUM_PLANES_PROCESSED)</span></div><div class="line"><a name="l01847"></a><span class="lineno"> 1847</span> <span class="preprocessor">#endif // defined(VEC_SIZE) && defined(SRC_DIM_2) && defined(CONV_PAD_TOP) && defined(CONV_PAD_LEFT) && defined(DATA_TYPE)</span></div><div class="ttc" id="struct_vector_xhtml"><div class="ttname"><a href="struct_vector.xhtml">Vector</a></div><div class="ttdoc">Structure to hold Vector information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00341">helpers.h:341</a></div></div> |