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| <a href="convolution5x5_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) 2016-2019 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">#ifndef DATA_TYPE</span></div><div class="line"><a name="l00027"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1"> 27</a></span> <span class="preprocessor">#define DATA_TYPE short</span></div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#endif </span><span class="comment">/* DATA_TYPE */</span><span class="preprocessor"></span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#ifndef COMPUTE_TYPE</span></div><div class="line"><a name="l00031"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875"> 31</a></span> <span class="preprocessor">#define COMPUTE_TYPE int</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#endif </span><span class="comment">/* COMPUTE_TYPE */</span><span class="preprocessor"></span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#ifndef DATA_TYPE_OUT</span></div><div class="line"><a name="l00035"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#ac06f3e24d3fffd3c465d8b2a6e7c985e"> 35</a></span> <span class="preprocessor">#define DATA_TYPE_OUT uchar</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#endif </span><span class="comment">/* DATA_TYPE_OUT */</span><span class="preprocessor"></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"> 38</span> <span class="comment">/** Compute a 1D horizontal convolution of size 5 for 8 bytes assuming the input is made of 1 channel of 1 byte (i.e 8 pixels).</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment"> *</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment"> * @param[in] left_pixel Pointer to the left pixel</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"> * @param[in] left1_coeff Weight of the most left pixel</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment"> * @param[in] left2_coeff Weight of the left pixel</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"> * @param[in] right1_coeff Weight of the right pixel</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment"> * @param[in] right2_coeff Weight of the most right pixel</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"> *</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment"> * @return a short8 containing 8 convoluted values.</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"> */</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</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="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688"> 50</a></span> <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  __global const uchar *left_pixel,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  const <span class="keywordtype">short</span> left1_coeff,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  const <span class="keywordtype">short</span> left2_coeff,</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  const <span class="keywordtype">short</span> middle_coeff,</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  const <span class="keywordtype">short</span> right1_coeff,</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  const <span class="keywordtype">short</span> right2_coeff)</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> {</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  uchar16 temp = vload16(0, left_pixel);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  left1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s01234567, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8));</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</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="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  left2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s12345678, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8));</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</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="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  middle = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s23456789, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8));</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</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="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  right1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s3456789a, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8));</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</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="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  right2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(temp.s456789ab, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8));</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordflow">return</span> left1 * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8))left1_coeff + left2 * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8))left2_coeff</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  + middle * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8))middle_coeff + right1 * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8))right1_coeff + right2 * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8))right2_coeff;</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span> }</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span> <span class="comment"></span></div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> <span class="comment">/** Compute a 1D vertical convolution of size 5 for 8 bytes assuming the input is made of 1 channel of 1 byte (i.e 8 pixels).</span></div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> <span class="comment"> *</span></div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span> <span class="comment"> * @param[in] src Pointer to source image.</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> <span class="comment"> * @param[in] up1_coeff Weight of the most up pixel</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> <span class="comment"> * @param[in] up2_coeff Weight of the up pixel</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span> <span class="comment"> * @param[in] middle_coeff Weight of the middle pixel</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="comment"> * @param[in] down1_coeff Weight of the down pixel</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span> <span class="comment"> * @param[in] down2_coeff Weight of the most down pixel</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="comment"> *</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> <span class="comment"> * @return a short8 containing 8 convoluted values.</span></div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> <span class="comment"> */</span></div><div class="line"><a name="l00086"></a><span class="lineno"> 86</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="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8)</div><div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#af8d82290182db5e89de313121bbb22d2"> 87</a></span> <a class="code" href="convolution5x5_8cl.xhtml#af8d82290182db5e89de313121bbb22d2">convolution5x1</a>(</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</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="l00089"></a><span class="lineno"> 89</span>  const <span class="keywordtype">short</span> up1_coeff,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  const <span class="keywordtype">short</span> up2_coeff,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  const <span class="keywordtype">short</span> middle_coeff,</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  const <span class="keywordtype">short</span> down1_coeff,</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  const <span class="keywordtype">short</span> down2_coeff)</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span> {</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8)</div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  val;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</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="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8)</div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  out = (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))0;</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span> </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  val = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(vload8(0, (__global <a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<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, -2)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8));</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  out += val * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))up1_coeff;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  val = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(vload8(0, (__global <a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<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, -1)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8));</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  out += val * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))up2_coeff;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span> </div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  val = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(vload8(0, (__global <a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<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)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8));</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  out += val * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))middle_coeff;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span> </div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  val = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(vload8(0, (__global <a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<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, 1)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8));</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  out += val * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))down1_coeff;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> </div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  val = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>(vload8(0, (__global <a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<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, 2)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8));</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  out += val * (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))down2_coeff;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordflow">return</span> out;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> }</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span> <span class="comment"></span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> <span class="comment">/** Apply a 5x5 convolution matrix to a single channel U8 input image and return the result.</span></div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> <span class="comment"> *</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment"> * Convolution matrix layout:\n</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="comment"> * [ mat0, mat1, mat2, mat3 , mat4 ]\n</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span> <span class="comment"> * [ mat5, mat6, mat7, mat8, mat9 ]\n</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> <span class="comment"> * [ mat10, mat11, mat12, mat13, mat14 ]\n</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <span class="comment"> * [ mat15, mat16, mat17, mat18, mat19 ]\n</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="comment"> * [ mat20, mat21, mat22, mat23, mat24 ]</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span> <span class="comment"> *</span></div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="comment"> * @param[in] src A pointer to source Image structure.</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span> <span class="comment"> * @param[in] mat0 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="comment"> * @param[in] mat1 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span> <span class="comment"> * @param[in] mat2 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span> <span class="comment"> * @param[in] mat3 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> <span class="comment"> * @param[in] mat4 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="comment"> * @param[in] mat5 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span> <span class="comment"> * @param[in] mat6 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span> <span class="comment"> * @param[in] mat7 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="comment"> * @param[in] mat8 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> <span class="comment"> * @param[in] mat9 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="comment"> * @param[in] mat10 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> <span class="comment"> * @param[in] mat11 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="comment"> * @param[in] mat12 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="comment"> * @param[in] mat13 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="comment"> * @param[in] mat14 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="comment"> * @param[in] mat15 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment"> * @param[in] mat16 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment"> * @param[in] mat17 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment"> * @param[in] mat18 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment"> * @param[in] mat19 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment"> * @param[in] mat20 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment"> * @param[in] mat21 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="comment"> * @param[in] mat22 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="comment"> * @param[in] mat23 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="comment"> * @param[in] mat24 Coefficient from the convolution matrix</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="comment"> * @param[in] scale Convolution matrix scale (Sum of the coefficients, or 1 if the sum is 0)</span></div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span> <span class="comment"> *</span></div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> <span class="comment"> * @return a short8 containing 8 convoluted and scaled values.</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span> <span class="comment"> */</span></div><div class="line"><a name="l00157"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#ac80109c09492de142b4b7498f2fc6abb"> 157</a></span> short8 <a class="code" href="convolution5x5_8cl.xhtml#ac80109c09492de142b4b7498f2fc6abb">convolution5x5</a>(</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</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="l00159"></a><span class="lineno"> 159</span>  <span class="keyword">const</span> <span class="keywordtype">short</span> mat0, <span class="keyword">const</span> <span class="keywordtype">short</span> mat1, <span class="keyword">const</span> <span class="keywordtype">short</span> mat2, <span class="keyword">const</span> <span class="keywordtype">short</span> mat3, <span class="keyword">const</span> <span class="keywordtype">short</span> mat4,</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">const</span> <span class="keywordtype">short</span> mat5, <span class="keyword">const</span> <span class="keywordtype">short</span> mat6, <span class="keyword">const</span> <span class="keywordtype">short</span> mat7, <span class="keyword">const</span> <span class="keywordtype">short</span> mat8, <span class="keyword">const</span> <span class="keywordtype">short</span> mat9,</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">const</span> <span class="keywordtype">short</span> mat10, <span class="keyword">const</span> <span class="keywordtype">short</span> mat11, <span class="keyword">const</span> <span class="keywordtype">short</span> mat12, <span class="keyword">const</span> <span class="keywordtype">short</span> mat13, <span class="keyword">const</span> <span class="keywordtype">short</span> mat14,</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">const</span> <span class="keywordtype">short</span> mat15, <span class="keyword">const</span> <span class="keywordtype">short</span> mat16, <span class="keyword">const</span> <span class="keywordtype">short</span> mat17, <span class="keyword">const</span> <span class="keywordtype">short</span> mat18, <span class="keyword">const</span> <span class="keywordtype">short</span> mat19,</div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="keyword">const</span> <span class="keywordtype">short</span> mat20, <span class="keyword">const</span> <span class="keywordtype">short</span> mat21, <span class="keyword">const</span> <span class="keywordtype">short</span> mat22, <span class="keyword">const</span> <span class="keywordtype">short</span> mat23, <span class="keyword">const</span> <span class="keywordtype">short</span> mat24,</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  uint <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>)</div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span> {</div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  pixels;</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span> </div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  pixels = <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(<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>, -2, -2), mat0, mat1, mat2, mat3, mat4);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  pixels += <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(<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>, -2, -1), mat5, mat6, mat7, mat8, mat9);</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  pixels += <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(<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>, -2, 0), mat10, mat11, mat12, mat13, mat14);</div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  pixels += <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(<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>, -2, 1), mat15, mat16, mat17, mat18, mat19);</div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  pixels += <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(<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>, -2, 2), mat20, mat21, mat22, mat23, mat24);</div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> </div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a> > 0)</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  {</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  pixels /= (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8))<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">scale</a>;</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  }</div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span> </div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="keywordflow">return</span> convert_short8_sat(pixels);</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> }</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span> </div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> <span class="preprocessor">#ifndef DYNAMIC_MATRIX_CONVOLUTION</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> <span class="comment"></span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span> <span class="comment">/** Apply a 1x5 static convolution matrix to a single channel U8 input image and output a single temporary channel image(Support U16, S16, S32).</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> <span class="comment"> *</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span> <span class="comment"> * @attention The matrix coefficients (MAT0, MAT1, MAT2, MAT3, MAT4) and DATA_TYPE need to be passed at compile time:\n</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> <span class="comment"> * e.g. -DMAT0=1 -DMAT2=2, -DMAT3=3, -DMAT4=4, -DDATA_TYPE=int</span></div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span> <span class="comment"> *</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> <span class="comment"> * @param[in] src_ptr Pointer to the source image. Supported data types: U8</span></div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span> <span class="comment"> * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</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="l00193"></a><span class="lineno"> 193</span> <span class="comment"> * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</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="l00195"></a><span class="lineno"> 195</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image</span></div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> <span class="comment"> * @param[out] dst_ptr Pointer to the destination image. Supported data types: U16, S16, S32</span></div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes)</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</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="l00199"></a><span class="lineno"> 199</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes)</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</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="l00201"></a><span class="lineno"> 201</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image</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"><a class="line" href="convolution5x5_8cl.xhtml#a59312d2415e9c4baa221da9394ea4e3c"> 203</a></span> __kernel <span class="keywordtype">void</span> <a class="code" href="convolution5x5_8cl.xhtml#a59312d2415e9c4baa221da9394ea4e3c">convolution_separable1x5_static</a>(</div><div class="line"><a name="l00204"></a><span class="lineno"> 204</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#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</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="l00206"></a><span class="lineno"> 206</span> {</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</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#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00208"></a><span class="lineno"> 208</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#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span> </div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="comment">// Output pixels</span></div><div class="line"><a name="l00211"></a><span class="lineno"> 211</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="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>, 8)</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  pixels = <a class="code" href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a>(<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>, -2, 0), MAT0, MAT1, MAT2, MAT3, MAT4);</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span> </div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  <span class="comment">// Store result in dst</span></div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  vstore8(pixels, 0, (__global <a class="code" href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> }</div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span> <span class="comment"></span></div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="comment">/** Apply a 5x1 static convolution matrix to a single channel U8 input image and output a single channel image.</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="comment"> *</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span> <span class="comment"> * @attention The matrix coefficients (MAT5, MAT6, MAT7, MAT8, MAT9, SCALE), COMPUTE_TYPE and DATA_TYPE_OUT need to be passed at compile time:\n</span></div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> <span class="comment"> * e.g. -DMAT5=1 -DMAT6=2, -DMAT7=3, -DMAT8=4, -DMAT9=5, -DSCALE=6, -DCOMPUTE_TYPE=int, -DDATA_TYPE_OUT=int</span></div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span> <span class="comment"> *</span></div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span> <span class="comment"> * @param[in] src_ptr Pointer to the source image. Supported data types: U16, S16, S32</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span> <span class="comment"> * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)</span></div><div class="line"><a name="l00225"></a><span class="lineno"> 225</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="l00226"></a><span class="lineno"> 226</span> <span class="comment"> * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00227"></a><span class="lineno"> 227</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="l00228"></a><span class="lineno"> 228</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image</span></div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="comment"> * @param[out] dst_ptr Pointer to the destination image. Supported data types: U8, S16</span></div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes)</span></div><div class="line"><a name="l00231"></a><span class="lineno"> 231</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="l00232"></a><span class="lineno"> 232</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes)</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</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="l00234"></a><span class="lineno"> 234</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image</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"><a class="line" href="convolution5x5_8cl.xhtml#afafa1a261166012b37a2cae3130b6b33"> 236</a></span> __kernel <span class="keywordtype">void</span> <a class="code" href="convolution5x5_8cl.xhtml#afafa1a261166012b37a2cae3130b6b33">convolution_separable5x1_static</a>(</div><div class="line"><a name="l00237"></a><span class="lineno"> 237</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#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</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="l00239"></a><span class="lineno"> 239</span> {</div><div class="line"><a name="l00240"></a><span class="lineno"> 240</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#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00241"></a><span class="lineno"> 241</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#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> </div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// Output pixels</span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</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="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8)</div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  pixels = <a class="code" href="convolution5x5_8cl.xhtml#af8d82290182db5e89de313121bbb22d2">convolution5x1</a>(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>, MAT5, MAT6, MAT7, MAT8, MAT9);</div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> </div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  <span class="comment">// Divide by the scale</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  pixels /= (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a>, 8))SCALE;</div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> </div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="comment">// Store result in dst</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  vstore8(<a class="code" href="direct__convolution1x1_8cl.xhtml#a1f15728672380ade7a238f5e783d54d2">CONVERT_SAT</a>(pixels, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#ac06f3e24d3fffd3c465d8b2a6e7c985e">DATA_TYPE_OUT</a>, 8)), 0, (__global <a class="code" href="convolution5x5_8cl.xhtml#ac06f3e24d3fffd3c465d8b2a6e7c985e">DATA_TYPE_OUT</a> *)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr);</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="comment"></span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="comment">/** Apply a static 5x5 convolution matrix to a single channel U8 input image and output a single channel image including borders</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="comment"> *</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</span> <span class="comment"> * @attention The matrix coefficients(MAT0, MAT1, ... MAT24, SCALE), DATA_TYPE_OUT need to be passed at compile time:\n</span></div><div class="line"><a name="l00257"></a><span class="lineno"> 257</span> <span class="comment"> * e.g. -DMAT0=1 -DMAT1=2, ... -DMAT24=24, -DSCALE=6, -DDATA_TYPE_OUT=int</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"> * @param[in] src_ptr Pointer to the source image. Supported data types: U8</span></div><div class="line"><a name="l00260"></a><span class="lineno"> 260</span> <span class="comment"> * @param[in] src_stride_x Stride of the source image in X dimension (in bytes)</span></div><div class="line"><a name="l00261"></a><span class="lineno"> 261</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="l00262"></a><span class="lineno"> 262</span> <span class="comment"> * @param[in] src_stride_y Stride of the source image in Y dimension (in bytes)</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</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="l00264"></a><span class="lineno"> 264</span> <span class="comment"> * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image</span></div><div class="line"><a name="l00265"></a><span class="lineno"> 265</span> <span class="comment"> * @param[out] dst_ptr Pointer to the destination image. Supported data types: U8, S16</span></div><div class="line"><a name="l00266"></a><span class="lineno"> 266</span> <span class="comment"> * @param[in] dst_stride_x Stride of the destination image in X dimension (in bytes)</span></div><div class="line"><a name="l00267"></a><span class="lineno"> 267</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="l00268"></a><span class="lineno"> 268</span> <span class="comment"> * @param[in] dst_stride_y Stride of the destination image in Y dimension (in bytes)</span></div><div class="line"><a name="l00269"></a><span class="lineno"> 269</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="l00270"></a><span class="lineno"> 270</span> <span class="comment"> * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination image</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</span> <span class="comment"> */</span></div><div class="line"><a name="l00272"></a><span class="lineno"><a class="line" href="convolution5x5_8cl.xhtml#a643ab36ceadc54b36cb5b73ef0154913"> 272</a></span> __kernel <span class="keywordtype">void</span> <a class="code" href="convolution5x5_8cl.xhtml#a643ab36ceadc54b36cb5b73ef0154913">convolution5x5_static</a>(</div><div class="line"><a name="l00273"></a><span class="lineno"> 273</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#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00274"></a><span class="lineno"> 274</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="l00275"></a><span class="lineno"> 275</span> {</div><div class="line"><a name="l00276"></a><span class="lineno"> 276</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#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>);</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</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#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>);</div><div class="line"><a name="l00278"></a><span class="lineno"> 278</span> </div><div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  short8 pixels = <a class="code" href="convolution5x5_8cl.xhtml#ac80109c09492de142b4b7498f2fc6abb">convolution5x5</a>(&<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>,</div><div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  MAT0, MAT1, MAT2, MAT3, MAT4, MAT5, MAT6, MAT7, MAT8, MAT9, MAT10, MAT11, MAT12, MAT13,</div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  MAT14, MAT15, MAT16, MAT17, MAT18, MAT19, MAT20, MAT21, MAT22, MAT23, MAT24, SCALE);</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span> </div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  <span class="comment">// Store the result as is in dst</span></div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  vstore8(<a class="code" href="direct__convolution1x1_8cl.xhtml#a1f15728672380ade7a238f5e783d54d2">CONVERT_SAT</a>(pixels, <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(<a class="code" href="convolution5x5_8cl.xhtml#ac06f3e24d3fffd3c465d8b2a6e7c985e">DATA_TYPE_OUT</a>, 8)), 0, (__global <a class="code" href="convolution5x5_8cl.xhtml#ac06f3e24d3fffd3c465d8b2a6e7c985e">DATA_TYPE_OUT</a> *)<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">dst</a>.ptr);</div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span> }</div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span> </div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span> <span class="preprocessor">#endif // DYNAMIC_MATRIX_CONVOLUTION</span></div><div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a009469e4d9b8fce3b6d5e97d2077827d"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a></div><div class="ttdeci">__global uchar * offset(const Image *img, int x, int y)</div><div class="ttdoc">Get the pointer position of a Image.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00510">helpers.h:510</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_ac80109c09492de142b4b7498f2fc6abb"><div class="ttname"><a href="convolution5x5_8cl.xhtml#ac80109c09492de142b4b7498f2fc6abb">convolution5x5</a></div><div class="ttdeci">short8 convolution5x5(Image *src, const short mat0, const short mat1, const short mat2, const short mat3, const short mat4, const short mat5, const short mat6, const short mat7, const short mat8, const short mat9, const short mat10, const short mat11, const short mat12, const short mat13, const short mat14, const short mat15, const short mat16, const short mat17, const short mat18, const short mat19, const short mat20, const short mat21, const short mat22, const short mat23, const short mat24, uint scale)</div><div class="ttdoc">Apply a 5x5 convolution matrix to a single channel U8 input image and return the result.</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00157">convolution5x5.cl:157</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_aa8d95ba04fc73845abc6045952cae5be"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a></div><div class="ttdeci">#define CONVERT(x, type)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00261">helpers.h:261</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_aebe814363556c244be043b13e7969197"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aebe814363556c244be043b13e7969197">CONVERT_TO_IMAGE_STRUCT</a></div><div class="ttdeci">#define CONVERT_TO_IMAGE_STRUCT(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00311">helpers.h:311</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_a643ab36ceadc54b36cb5b73ef0154913"><div class="ttname"><a href="convolution5x5_8cl.xhtml#a643ab36ceadc54b36cb5b73ef0154913">convolution5x5_static</a></div><div class="ttdeci">__kernel void convolution5x5_static(__global uchar *src_ptr, uint src_stride_x, uint src_step_x, uint src_stride_y, uint src_step_y, uint src_offset_first_element_in_bytes, __global uchar *dst_ptr, uint dst_stride_x, uint dst_step_x, uint dst_stride_y, uint dst_step_y, uint dst_offset_first_element_in_bytes)</div><div class="ttdoc">Apply a static 5x5 convolution matrix to a single channel U8 input image and output a single channel ...</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00272">convolution5x5.cl:272</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_af8d82290182db5e89de313121bbb22d2"><div class="ttname"><a href="convolution5x5_8cl.xhtml#af8d82290182db5e89de313121bbb22d2">convolution5x1</a></div><div class="ttdeci">int8 convolution5x1(Image *src, const short up1_coeff, const short up2_coeff, const short middle_coeff, const short down1_coeff, const short down2_coeff)</div><div class="ttdoc">Compute a 1D vertical convolution of size 5 for 8 bytes assuming the input is made of 1 channel of 1 ...</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00087">convolution5x5.cl:87</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_a26babb0c719990ecbdf3abc6de920875"><div class="ttname"><a href="convolution5x5_8cl.xhtml#a26babb0c719990ecbdf3abc6de920875">COMPUTE_TYPE</a></div><div class="ttdeci">#define COMPUTE_TYPE</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00031">convolution5x5.cl:31</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a22f42fcf2077d951271df83b55c1a71a"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a22f42fcf2077d951271df83b55c1a71a">IMAGE_DECLARATION</a></div><div class="ttdeci">#define IMAGE_DECLARATION(name)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00275">helpers.h:275</a></div></div> |
| <div class="ttc" id="direct__convolution1x1_8cl_xhtml_a1f15728672380ade7a238f5e783d54d2"><div class="ttname"><a href="direct__convolution1x1_8cl.xhtml#a1f15728672380ade7a238f5e783d54d2">CONVERT_SAT</a></div><div class="ttdeci">#define CONVERT_SAT(a, b)</div><div class="ttdef"><b>Definition:</b> <a href="direct__convolution1x1_8cl_source.xhtml#l00030">direct_convolution1x1.cl:30</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_abd315efadde56274531a8b2bdb43f688"><div class="ttname"><a href="convolution5x5_8cl.xhtml#abd315efadde56274531a8b2bdb43f688">convolution1x5</a></div><div class="ttdeci">short8 convolution1x5(__global const uchar *left_pixel, const short left1_coeff, const short left2_coeff, const short middle_coeff, const short right1_coeff, const short right2_coeff)</div><div class="ttdoc">Compute a 1D horizontal convolution of size 5 for 8 bytes assuming the input is made of 1 channel of ...</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00050">convolution5x5.cl:50</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_adbf67dcee294e673cf796f1ed8aeb6a4"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#adbf67dcee294e673cf796f1ed8aeb6a4">arm_compute::test::validation::dst</a></div><div class="ttdeci">CLTensor dst</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_absolute_difference_8cpp_source.xhtml#l00102">AbsoluteDifference.cpp:102</a></div></div> |
| <div class="ttc" id="struct_image_xhtml"><div class="ttname"><a href="struct_image.xhtml">Image</a></div><div class="ttdoc">Structure to hold Image information.</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00349">helpers.h:349</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_a59312d2415e9c4baa221da9394ea4e3c"><div class="ttname"><a href="convolution5x5_8cl.xhtml#a59312d2415e9c4baa221da9394ea4e3c">convolution_separable1x5_static</a></div><div class="ttdeci">__kernel void convolution_separable1x5_static(__global uchar *src_ptr, uint src_stride_x, uint src_step_x, uint src_stride_y, uint src_step_y, uint src_offset_first_element_in_bytes, __global uchar *dst_ptr, uint dst_stride_x, uint dst_step_x, uint dst_stride_y, uint dst_step_y, uint dst_offset_first_element_in_bytes)</div><div class="ttdoc">Apply a 1x5 static convolution matrix to a single channel U8 input image and output a single temporar...</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00203">convolution5x5.cl:203</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_acec6d8ad52a28972fa74e071c1a63b6a"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#acec6d8ad52a28972fa74e071c1a63b6a">arm_compute::test::validation::scale</a></div><div class="ttdeci">scale</div><div class="ttdef"><b>Definition:</b> <a href="_n_e_o_n_2_pixel_wise_multiplication_8cpp_source.xhtml#l00375">PixelWiseMultiplication.cpp:375</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml">helpers.h</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_ac06f3e24d3fffd3c465d8b2a6e7c985e"><div class="ttname"><a href="convolution5x5_8cl.xhtml#ac06f3e24d3fffd3c465d8b2a6e7c985e">DATA_TYPE_OUT</a></div><div class="ttdeci">#define DATA_TYPE_OUT</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00035">convolution5x5.cl:35</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_afb8c72ce35c4a1f4a2588d6573e54aa1"><div class="ttname"><a href="convolution5x5_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="ttdeci">#define DATA_TYPE</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00027">convolution5x5.cl:27</a></div></div> |
| <div class="ttc" id="convolution5x5_8cl_xhtml_afafa1a261166012b37a2cae3130b6b33"><div class="ttname"><a href="convolution5x5_8cl.xhtml#afafa1a261166012b37a2cae3130b6b33">convolution_separable5x1_static</a></div><div class="ttdeci">__kernel void convolution_separable5x1_static(__global uchar *src_ptr, uint src_stride_x, uint src_step_x, uint src_stride_y, uint src_step_y, uint src_offset_first_element_in_bytes, __global uchar *dst_ptr, uint dst_stride_x, uint dst_step_x, uint dst_stride_y, uint dst_step_y, uint dst_offset_first_element_in_bytes)</div><div class="ttdoc">Apply a 5x1 static convolution matrix to a single channel U8 input image and output a single channel ...</div><div class="ttdef"><b>Definition:</b> <a href="convolution5x5_8cl_source.xhtml#l00236">convolution5x5.cl:236</a></div></div> |
| <div class="ttc" id="namespacearm__compute_1_1test_1_1validation_xhtml_a989ab3e96426615bb98e04e0235088ca"><div class="ttname"><a href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">arm_compute::test::validation::src</a></div><div class="ttdeci">cast configure & src</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_2_cast_8cpp_source.xhtml#l00169">Cast.cpp:169</a></div></div> |
| <div class="ttc" id="src_2core_2_c_l_2cl__kernels_2_helpers_8h_xhtml_a36f754c05b6fddf6df0d8d0a74f8159f"><div class="ttname"><a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a></div><div class="ttdeci">#define VEC_DATA_TYPE(type, size)</div><div class="ttdef"><b>Definition:</b> <a href="src_2core_2_c_l_2cl__kernels_2_helpers_8h_source.xhtml#l00255">helpers.h:255</a></div></div> |
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