| <a href="im2col_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) 2018 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">#if defined(DATA_TYPE) && defined(ELEMENT_SIZE)</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span> </div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span> <span class="preprocessor">#if ELEMENT_SIZE == 1</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#define COND_DATA_TYPE char</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> <span class="preprocessor">#elif ELEMENT_SIZE == 2</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#define COND_DATA_TYPE short</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#elif ELEMENT_SIZE == 4</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#define COND_DATA_TYPE int</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#else // ELEMENT_SIZE</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#error "Element size not support"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="preprocessor">#endif // ELEMENT_SIZE</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> </div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="preprocessor">#if defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)</span></div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span> <span class="comment">/** This opencl kernel performs im2col when the kernel size is 1x1, the stride_x = 1 and the data layout is NCHW</span></div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> <span class="comment"> *</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span> <span class="comment"> * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3</span></div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> <span class="comment"> * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="comment"> * @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span> <span class="comment"> *</span></div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l00049"></a><span class="lineno"> 49</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="l00050"></a><span class="lineno"> 50</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="l00051"></a><span class="lineno"> 51</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="l00052"></a><span class="lineno"> 52</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="l00053"></a><span class="lineno"> 53</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="l00054"></a><span class="lineno"> 54</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</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="l00056"></a><span class="lineno"> 56</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="l00057"></a><span class="lineno"> 57</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="l00058"></a><span class="lineno"> 58</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="l00059"></a><span class="lineno"> 59</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="l00060"></a><span class="lineno"> 60</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="l00061"></a><span class="lineno"> 61</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="l00062"></a><span class="lineno"> 62</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00063"></a><span class="lineno"> 63</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="l00064"></a><span class="lineno"> 64</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="l00065"></a><span class="lineno"> 65</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="l00066"></a><span class="lineno"> 66</span> <span class="comment"> */</span></div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span> __kernel <span class="keywordtype">void</span> im2col1x1_stridex1_nchw(</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#a6b83038822d1ae7ab619b684ed3b7fc0">TENSOR3D_DECLARATION</a>(<a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#a989ab3e96426615bb98e04e0235088ca">src</a>),</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span> #<span class="keywordflow">if</span> defined(NUM_GROUPS)</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</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="l00071"></a><span class="lineno"> 71</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</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="l00073"></a><span class="lineno"> 73</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  uint src_stride_w,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  uint dst_stride_w)</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> {</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="keyword">const</span> uint xc = get_global_id(0) * 4; <span class="comment">// x coordinate in the convolved tensor</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keyword">const</span> uint yc = get_global_id(1); <span class="comment">// y coordinate in the convolved tensor</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="keyword">const</span> uint ch = get_global_id(2) % SRC_DEPTH; <span class="comment">// input feature map</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keyword">const</span> uint batch = get_global_id(2) / SRC_DEPTH; <span class="comment">// batch size</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// Clamp xc</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="comment">// The strategy clamps at "xc" as it will be a valid value for sure</span></div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  uint4 xc_clamped = xc + (uint4)(0, 1, 2, 3);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="comment">// Check which values are valid</span></div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keyword">const</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4) cond0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((xc_clamped < SRC_WIDTH), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4));</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  xc_clamped = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((uint4)xc, xc_clamped, convert_int4(cond0));</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  const uint xi = xc;</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  const uint yi = yc * STRIDE_Y;</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>  <span class="comment">// Calculate output indices</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">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="keyword">const</span> uint xo = ch % (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keyword">const</span> uint zo = ch / (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keyword">const</span> uint xo = ch;</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> uint4 yo = xc_clamped + yc * CONVOLVED_WIDTH; <span class="comment">// Index of the convolution</span></div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_x + yi * src_stride_y + ch * src_stride_z + batch * src_stride_w;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + zo * dst_stride_z + batch * dst_stride_w;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + batch * dst_stride_w;</div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  <a class="code" href="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>, 4)</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  data = vload4(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr);</div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> </div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  <span class="comment">// If out-of-bound, overwrite with the first element</span></div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  data = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 4))data.s0, data, cond0);</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span> </div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  *(__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s0 * dst_stride_y) = data.s0;</div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  *(__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s1 * dst_stride_y) = data.s1;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  *(__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s2 * dst_stride_y) = data.s2;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  *(__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s3 * dst_stride_y) = data.s3;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span> </div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <span class="keywordflow">if</span>(xo == (SRC_DEPTH / NUM_GROUPS - 1))</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="keywordflow">if</span>(ch == (SRC_DEPTH - 1))</div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  {</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s0 * dst_stride_y) + 1) = 1.0f;</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s1 * dst_stride_y) + 1) = 1.0f;</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s2 * dst_stride_y) + 1) = 1.0f;</div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr + yo.s3 * dst_stride_y) + 1) = 1.0f;</div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  }</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> }</div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span> <span class="preprocessor">#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_Y) && defined(SRC_DEPTH)</span></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span> </div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> <span class="preprocessor">#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)</span></div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> <span class="preprocessor">#if defined(DILATION_X) && defined(DILATION_Y)</span></div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> <span class="comment">/** This opencl kernel performs a generic im2col implementation when the data layout is NCHW</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span> <span class="comment"> *</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="comment"> * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> <span class="comment"> * @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64</span></div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> <span class="comment"> * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2</span></div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span> <span class="comment"> * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0</span></div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span> <span class="comment"> * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1</span></div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> <span class="comment"> * @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1</span></div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> <span class="comment"> * @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4</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"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l00156"></a><span class="lineno"> 156</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="l00157"></a><span class="lineno"> 157</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="l00158"></a><span class="lineno"> 158</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="l00159"></a><span class="lineno"> 159</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="l00160"></a><span class="lineno"> 160</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="l00161"></a><span class="lineno"> 161</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00162"></a><span class="lineno"> 162</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="l00163"></a><span class="lineno"> 163</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="l00164"></a><span class="lineno"> 164</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="l00165"></a><span class="lineno"> 165</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="l00166"></a><span class="lineno"> 166</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="l00167"></a><span class="lineno"> 167</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="l00168"></a><span class="lineno"> 168</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="l00169"></a><span class="lineno"> 169</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00170"></a><span class="lineno"> 170</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="l00171"></a><span class="lineno"> 171</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="l00172"></a><span class="lineno"> 172</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="l00173"></a><span class="lineno"> 173</span> <span class="comment"> */</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span> __kernel <span class="keywordtype">void</span> im2col_generic_nchw(</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</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="l00176"></a><span class="lineno"> 176</span> #<span class="keywordflow">if</span> defined(NUM_GROUPS)</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</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="l00178"></a><span class="lineno"> 178</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</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="l00180"></a><span class="lineno"> 180</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  uint src_stride_w,</div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  uint dst_stride_w)</div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span> {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xc = get_global_id(0); <span class="comment">// x coordinate in the convolved tensor</span></div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yc = get_global_id(1); <span class="comment">// y coordinate in the convolved tensor</span></div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = get_global_id(2) % SRC_DEPTH; <span class="comment">// input feature map</span></div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / SRC_DEPTH; <span class="comment">// batch size</span></div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span> </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = xc * STRIDE_X - PAD_LEFT;</div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = yc * STRIDE_Y - PAD_TOP;</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">// Calculate output indices</span></div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * KERNEL_WIDTH * KERNEL_HEIGHT;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> zo = ch / (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = xc + yc * CONVOLVED_WIDTH; <span class="comment">// Index of the convolution</span></div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;</div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  __global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *output_ptr = ((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w)) + xo;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  __global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *output_ptr = ((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;</div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span> </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="comment">// Linearize convolution elements</span></div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yk = 0; yk < KERNEL_HEIGHT; ++yk)</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  {</div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <span class="keywordtype">int</span> y = yi + yk * DILATION_Y;</div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xk = 0; xk < KERNEL_WIDTH; ++xk, ++output_ptr)</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  {</div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  <span class="keywordtype">int</span> x = xi + xk * DILATION_X;</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> <span class="preprocessor">#if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0</span></div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  *output_ptr = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + x * src_stride_x + y * src_stride_y));</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> <span class="preprocessor">#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0</span></div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  <span class="keywordflow">if</span>(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT)</div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  {</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  *output_ptr = PAD_VALUE;</div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  }</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  {</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span>  *output_ptr = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + x * src_stride_x + y * src_stride_y));</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span>  }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="preprocessor">#endif // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0</span></div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span>  }</div><div class="line"><a name="l00229"></a><span class="lineno"> 229</span>  }</div><div class="line"><a name="l00230"></a><span class="lineno"> 230</span> </div><div class="line"><a name="l00231"></a><span class="lineno"> 231</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00232"></a><span class="lineno"> 232</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  <span class="keywordflow">if</span>((xo / (KERNEL_WIDTH * KERNEL_HEIGHT)) == (SRC_DEPTH / NUM_GROUPS - 1))</div><div class="line"><a name="l00234"></a><span class="lineno"> 234</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="keywordflow">if</span>(ch == (SRC_DEPTH - 1))</div><div class="line"><a name="l00236"></a><span class="lineno"> 236</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  {</div><div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  *output_ptr = 1.0f;</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> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l00241"></a><span class="lineno"> 241</span> }</div><div class="line"><a name="l00242"></a><span class="lineno"> 242</span> <span class="preprocessor">#endif // defined(DILATION_X) && defined(DILATION_Y)</span></div><div class="line"><a name="l00243"></a><span class="lineno"> 243</span> <span class="comment"></span></div><div class="line"><a name="l00244"></a><span class="lineno"> 244</span> <span class="comment">/** This opencl kernel performs im2col when the kernel size is 3x3 and the data layout is NCHW</span></div><div class="line"><a name="l00245"></a><span class="lineno"> 245</span> <span class="comment"> *</span></div><div class="line"><a name="l00246"></a><span class="lineno"> 246</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00247"></a><span class="lineno"> 247</span> <span class="comment"> * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128</span></div><div class="line"><a name="l00248"></a><span class="lineno"> 248</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l00249"></a><span class="lineno"> 249</span> <span class="comment"> * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3</span></div><div class="line"><a name="l00250"></a><span class="lineno"> 250</span> <span class="comment"> * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2</span></div><div class="line"><a name="l00251"></a><span class="lineno"> 251</span> <span class="comment"> * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0</span></div><div class="line"><a name="l00252"></a><span class="lineno"> 252</span> <span class="comment"> * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1</span></div><div class="line"><a name="l00253"></a><span class="lineno"> 253</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00254"></a><span class="lineno"> 254</span> <span class="comment"> *</span></div><div class="line"><a name="l00255"></a><span class="lineno"> 255</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l00256"></a><span class="lineno"> 256</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="l00257"></a><span class="lineno"> 257</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="l00258"></a><span class="lineno"> 258</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="l00259"></a><span class="lineno"> 259</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="l00260"></a><span class="lineno"> 260</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="l00261"></a><span class="lineno"> 261</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z 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_offset_first_element_in_bytes The offset of the first element in the source tensor</span></div><div class="line"><a name="l00263"></a><span class="lineno"> 263</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="l00264"></a><span class="lineno"> 264</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="l00265"></a><span class="lineno"> 265</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="l00266"></a><span class="lineno"> 266</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="l00267"></a><span class="lineno"> 267</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="l00268"></a><span class="lineno"> 268</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="l00269"></a><span class="lineno"> 269</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Z 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 tensor</span></div><div class="line"><a name="l00271"></a><span class="lineno"> 271</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="l00272"></a><span class="lineno"> 272</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="l00273"></a><span class="lineno"> 273</span> <span class="comment"> */</span></div><div class="line"><a name="l00274"></a><span class="lineno"> 274</span> __kernel <span class="keywordtype">void</span> im2col3x3_nchw(</div><div class="line"><a name="l00275"></a><span class="lineno"> 275</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="l00276"></a><span class="lineno"> 276</span> #<span class="keywordflow">if</span> defined(NUM_GROUPS)</div><div class="line"><a name="l00277"></a><span class="lineno"> 277</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="l00278"></a><span class="lineno"> 278</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00279"></a><span class="lineno"> 279</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="l00280"></a><span class="lineno"> 280</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  uint src_stride_w,</div><div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  uint dst_stride_w)</div><div class="line"><a name="l00283"></a><span class="lineno"> 283</span> {</div><div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xc = get_global_id(0); <span class="comment">// x coordinate in the convolved tensor</span></div><div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yc = get_global_id(1); <span class="comment">// y coordinate in the convolved tensor</span></div><div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = get_global_id(2) % SRC_DEPTH; <span class="comment">// input feature map</span></div><div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / SRC_DEPTH; <span class="comment">// batch size</span></div><div class="line"><a name="l00288"></a><span class="lineno"> 288</span> </div><div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = xc * STRIDE_X - PAD_LEFT;</div><div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = yc * STRIDE_Y - PAD_TOP;</div><div class="line"><a name="l00292"></a><span class="lineno"> 292</span> </div><div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="comment">// Calculate output indices</span></div><div class="line"><a name="l00294"></a><span class="lineno"> 294</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * 9; <span class="comment">// 3x3</span></div><div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> zo = ch / (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00297"></a><span class="lineno"> 297</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = ch * 9; <span class="comment">// 3x3</span></div><div class="line"><a name="l00299"></a><span class="lineno"> 299</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = xc + yc * CONVOLVED_WIDTH; <span class="comment">// Index of the convolution</span></div><div class="line"><a name="l00301"></a><span class="lineno"> 301</span> </div><div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * (int)src_stride_x + yi * (<span class="keywordtype">int</span>)src_stride_y + ch * src_stride_z + batch * src_stride_w;</div><div class="line"><a name="l00304"></a><span class="lineno"> 304</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w;</div><div class="line"><a name="l00306"></a><span class="lineno"> 306</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;</div><div class="line"><a name="l00308"></a><span class="lineno"> 308</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00309"></a><span class="lineno"> 309</span> </div><div class="line"><a name="l00310"></a><span class="lineno"> 310</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>, 3)</div><div class="line"><a name="l00311"></a><span class="lineno"> 311</span>  row0 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + 0 * src_stride_y));</div><div class="line"><a name="l00312"></a><span class="lineno"> 312</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>, 3)</div><div class="line"><a name="l00313"></a><span class="lineno"> 313</span>  row1 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + 1 * src_stride_y));</div><div class="line"><a name="l00314"></a><span class="lineno"> 314</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>, 3)</div><div class="line"><a name="l00315"></a><span class="lineno"> 315</span>  row2 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + 2 * src_stride_y));</div><div class="line"><a name="l00316"></a><span class="lineno"> 316</span> </div><div class="line"><a name="l00317"></a><span class="lineno"> 317</span> <span class="preprocessor">#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00318"></a><span class="lineno"> 318</span>  <span class="comment">// Put 0 if the value is out-of-bound</span></div><div class="line"><a name="l00319"></a><span class="lineno"> 319</span>  int3 x = (int3)xi + (int3)(0, 1, 2);</div><div class="line"><a name="l00320"></a><span class="lineno"> 320</span>  int3 y = (int3)yi + (int3)(0, 1, 2);</div><div class="line"><a name="l00321"></a><span class="lineno"> 321</span> </div><div class="line"><a name="l00322"></a><span class="lineno"> 322</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 3)</div><div class="line"><a name="l00323"></a><span class="lineno"> 323</span>  cond0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s0 >= 0 && y.s0 < SRC_HEIGHT)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 3));</div><div class="line"><a name="l00324"></a><span class="lineno"> 324</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 3)</div><div class="line"><a name="l00325"></a><span class="lineno"> 325</span>  cond1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s1 >= 0 && y.s1 < SRC_HEIGHT)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 3));</div><div class="line"><a name="l00326"></a><span class="lineno"> 326</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 3)</div><div class="line"><a name="l00327"></a><span class="lineno"> 327</span>  cond2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((x >= (int3)0 && x < (int3)SRC_WIDTH && (int3)(y.s2 >= 0 && y.s2 < SRC_HEIGHT)), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 3));</div><div class="line"><a name="l00328"></a><span class="lineno"> 328</span> </div><div class="line"><a name="l00329"></a><span class="lineno"> 329</span>  row0 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 3))PAD_VALUE, row0, cond0);</div><div class="line"><a name="l00330"></a><span class="lineno"> 330</span>  row1 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 3))PAD_VALUE, row1, cond1);</div><div class="line"><a name="l00331"></a><span class="lineno"> 331</span>  row2 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 3))PAD_VALUE, row2, cond2);</div><div class="line"><a name="l00332"></a><span class="lineno"> 332</span> <span class="preprocessor">#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00333"></a><span class="lineno"> 333</span> </div><div class="line"><a name="l00334"></a><span class="lineno"> 334</span>  vstore8((<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>, 8))(row0.s012, row1.s012, row2.s01), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00335"></a><span class="lineno"> 335</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8) = row2.s2;</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> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00338"></a><span class="lineno"> 338</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00339"></a><span class="lineno"> 339</span>  <span class="keywordflow">if</span>((xo / 9) == (SRC_DEPTH / NUM_GROUPS - 1))</div><div class="line"><a name="l00340"></a><span class="lineno"> 340</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00341"></a><span class="lineno"> 341</span>  <span class="keywordflow">if</span>(ch == (SRC_DEPTH - 1))</div><div class="line"><a name="l00342"></a><span class="lineno"> 342</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00343"></a><span class="lineno"> 343</span>  {</div><div class="line"><a name="l00344"></a><span class="lineno"> 344</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 9) = 1.0f;</div><div class="line"><a name="l00345"></a><span class="lineno"> 345</span>  }</div><div class="line"><a name="l00346"></a><span class="lineno"> 346</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l00347"></a><span class="lineno"> 347</span> }</div><div class="line"><a name="l00348"></a><span class="lineno"> 348</span> <span class="comment"></span></div><div class="line"><a name="l00349"></a><span class="lineno"> 349</span> <span class="comment">/** This opencl kernel performs im2col when the kernel size is 5x5 and the data layout is NCHW</span></div><div class="line"><a name="l00350"></a><span class="lineno"> 350</span> <span class="comment"> *</span></div><div class="line"><a name="l00351"></a><span class="lineno"> 351</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00352"></a><span class="lineno"> 352</span> <span class="comment"> * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128</span></div><div class="line"><a name="l00353"></a><span class="lineno"> 353</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l00354"></a><span class="lineno"> 354</span> <span class="comment"> * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3</span></div><div class="line"><a name="l00355"></a><span class="lineno"> 355</span> <span class="comment"> * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2</span></div><div class="line"><a name="l00356"></a><span class="lineno"> 356</span> <span class="comment"> * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0</span></div><div class="line"><a name="l00357"></a><span class="lineno"> 357</span> <span class="comment"> * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1</span></div><div class="line"><a name="l00358"></a><span class="lineno"> 358</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00359"></a><span class="lineno"> 359</span> <span class="comment"> * @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4</span></div><div class="line"><a name="l00360"></a><span class="lineno"> 360</span> <span class="comment"> *</span></div><div class="line"><a name="l00361"></a><span class="lineno"> 361</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l00362"></a><span class="lineno"> 362</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="l00363"></a><span class="lineno"> 363</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="l00364"></a><span class="lineno"> 364</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="l00365"></a><span class="lineno"> 365</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="l00366"></a><span class="lineno"> 366</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="l00367"></a><span class="lineno"> 367</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00368"></a><span class="lineno"> 368</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="l00369"></a><span class="lineno"> 369</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="l00370"></a><span class="lineno"> 370</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="l00371"></a><span class="lineno"> 371</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="l00372"></a><span class="lineno"> 372</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="l00373"></a><span class="lineno"> 373</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="l00374"></a><span class="lineno"> 374</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="l00375"></a><span class="lineno"> 375</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00376"></a><span class="lineno"> 376</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="l00377"></a><span class="lineno"> 377</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="l00378"></a><span class="lineno"> 378</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="l00379"></a><span class="lineno"> 379</span> <span class="comment"> */</span></div><div class="line"><a name="l00380"></a><span class="lineno"> 380</span> __kernel <span class="keywordtype">void</span> im2col5x5_nchw(</div><div class="line"><a name="l00381"></a><span class="lineno"> 381</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="l00382"></a><span class="lineno"> 382</span> #<span class="keywordflow">if</span> defined(NUM_GROUPS)</div><div class="line"><a name="l00383"></a><span class="lineno"> 383</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="l00384"></a><span class="lineno"> 384</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00385"></a><span class="lineno"> 385</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="l00386"></a><span class="lineno"> 386</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00387"></a><span class="lineno"> 387</span>  uint src_stride_w,</div><div class="line"><a name="l00388"></a><span class="lineno"> 388</span>  uint dst_stride_w)</div><div class="line"><a name="l00389"></a><span class="lineno"> 389</span> {</div><div class="line"><a name="l00390"></a><span class="lineno"> 390</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xc = get_global_id(0); <span class="comment">// x coordinate in the convolved tensor</span></div><div class="line"><a name="l00391"></a><span class="lineno"> 391</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yc = get_global_id(1); <span class="comment">// y coordinate in the convolved tensor</span></div><div class="line"><a name="l00392"></a><span class="lineno"> 392</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = get_global_id(2) % SRC_DEPTH; <span class="comment">// input feature map</span></div><div class="line"><a name="l00393"></a><span class="lineno"> 393</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / SRC_DEPTH; <span class="comment">// batch size</span></div><div class="line"><a name="l00394"></a><span class="lineno"> 394</span> </div><div class="line"><a name="l00395"></a><span class="lineno"> 395</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l00396"></a><span class="lineno"> 396</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = xc * STRIDE_X - PAD_LEFT;</div><div class="line"><a name="l00397"></a><span class="lineno"> 397</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = yc * STRIDE_Y - PAD_TOP;</div><div class="line"><a name="l00398"></a><span class="lineno"> 398</span> </div><div class="line"><a name="l00399"></a><span class="lineno"> 399</span>  <span class="comment">// Calculate output indices</span></div><div class="line"><a name="l00400"></a><span class="lineno"> 400</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00401"></a><span class="lineno"> 401</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * 25; <span class="comment">// 5x5</span></div><div class="line"><a name="l00402"></a><span class="lineno"> 402</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> zo = ch / (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00403"></a><span class="lineno"> 403</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00404"></a><span class="lineno"> 404</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = ch * 25; <span class="comment">// 5x5</span></div><div class="line"><a name="l00405"></a><span class="lineno"> 405</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00406"></a><span class="lineno"> 406</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = xc + yc * CONVOLVED_WIDTH; <span class="comment">// Index of the convolution</span></div><div class="line"><a name="l00407"></a><span class="lineno"> 407</span> </div><div class="line"><a name="l00408"></a><span class="lineno"> 408</span> <span class="preprocessor">#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00409"></a><span class="lineno"> 409</span>  <span class="comment">// Put 0 if the value is out-of-bound</span></div><div class="line"><a name="l00410"></a><span class="lineno"> 410</span>  int4 x0 = (int4)xi + (int4)(0, 1, 2, 3);</div><div class="line"><a name="l00411"></a><span class="lineno"> 411</span>  int4 y0 = (int4)yi + (int4)(0, 1, 2, 3);</div><div class="line"><a name="l00412"></a><span class="lineno"> 412</span>  <span class="keywordtype">int</span> x1 = xi + 4;</div><div class="line"><a name="l00413"></a><span class="lineno"> 413</span>  <span class="keywordtype">int</span> y1 = yi + 4;</div><div class="line"><a name="l00414"></a><span class="lineno"> 414</span> </div><div class="line"><a name="l00415"></a><span class="lineno"> 415</span>  <span class="comment">// Check if we could have out-of-bounds elements in the x direction</span></div><div class="line"><a name="l00416"></a><span class="lineno"> 416</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00417"></a><span class="lineno"> 417</span>  x0_condition = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((x0 >= (int4)0 && x0 < (int4)SRC_WIDTH), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4));</div><div class="line"><a name="l00418"></a><span class="lineno"> 418</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00419"></a><span class="lineno"> 419</span>  y0_condition = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aa8d95ba04fc73845abc6045952cae5be">CONVERT</a>((y0 >= (int4)0 && y0 < (int4)SRC_HEIGHT), <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4));</div><div class="line"><a name="l00420"></a><span class="lineno"> 420</span>  COND_DATA_TYPE x1_condition = (COND_DATA_TYPE)(x1 >= 0 && x1 < SRC_WIDTH);</div><div class="line"><a name="l00421"></a><span class="lineno"> 421</span>  COND_DATA_TYPE y1_condition = (COND_DATA_TYPE)(y1 >= 0 && y1 < SRC_HEIGHT);</div><div class="line"><a name="l00422"></a><span class="lineno"> 422</span> <span class="preprocessor">#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00423"></a><span class="lineno"> 423</span> </div><div class="line"><a name="l00424"></a><span class="lineno"> 424</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l00425"></a><span class="lineno"> 425</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * (int)src_stride_x + yi * (<span class="keywordtype">int</span>)src_stride_y + ch * src_stride_z + batch * src_stride_w;</div><div class="line"><a name="l00426"></a><span class="lineno"> 426</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00427"></a><span class="lineno"> 427</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w;</div><div class="line"><a name="l00428"></a><span class="lineno"> 428</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00429"></a><span class="lineno"> 429</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;</div><div class="line"><a name="l00430"></a><span class="lineno"> 430</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00431"></a><span class="lineno"> 431</span> </div><div class="line"><a name="l00432"></a><span class="lineno"> 432</span>  {</div><div class="line"><a name="l00433"></a><span class="lineno"> 433</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>, 4)</div><div class="line"><a name="l00434"></a><span class="lineno"> 434</span>  row00 = vload4(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr);</div><div class="line"><a name="l00435"></a><span class="lineno"> 435</span>  <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="line"><a name="l00436"></a><span class="lineno"> 436</span>  row01 = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr + 4);</div><div class="line"><a name="l00437"></a><span class="lineno"> 437</span> </div><div class="line"><a name="l00438"></a><span class="lineno"> 438</span>  input_ptr += src_stride_y;</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>  <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>, 4)</div><div class="line"><a name="l00441"></a><span class="lineno"> 441</span>  row10 = vload4(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr);</div><div class="line"><a name="l00442"></a><span class="lineno"> 442</span>  <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="line"><a name="l00443"></a><span class="lineno"> 443</span>  row11 = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr + 4);</div><div class="line"><a name="l00444"></a><span class="lineno"> 444</span> </div><div class="line"><a name="l00445"></a><span class="lineno"> 445</span> <span class="preprocessor">#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00446"></a><span class="lineno"> 446</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00447"></a><span class="lineno"> 447</span>  cond00 = x0_condition && (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4))y0_condition.s0;</div><div class="line"><a name="l00448"></a><span class="lineno"> 448</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00449"></a><span class="lineno"> 449</span>  cond10 = x0_condition && (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4))y0_condition.s1;</div><div class="line"><a name="l00450"></a><span class="lineno"> 450</span>  COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y0_condition.s0);</div><div class="line"><a name="l00451"></a><span class="lineno"> 451</span>  COND_DATA_TYPE cond11 = (COND_DATA_TYPE)(x1_condition && y0_condition.s1);</div><div class="line"><a name="l00452"></a><span class="lineno"> 452</span> </div><div class="line"><a name="l00453"></a><span class="lineno"> 453</span>  <span class="comment">// Replace with 0 if the value is not valid</span></div><div class="line"><a name="l00454"></a><span class="lineno"> 454</span>  row00 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 4))PAD_VALUE, row00, cond00);</div><div class="line"><a name="l00455"></a><span class="lineno"> 455</span>  row10 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 4))PAD_VALUE, row10, cond10);</div><div class="line"><a name="l00456"></a><span class="lineno"> 456</span>  row01 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>)PAD_VALUE, row01, cond01);</div><div class="line"><a name="l00457"></a><span class="lineno"> 457</span>  row11 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>)PAD_VALUE, row11, cond11);</div><div class="line"><a name="l00458"></a><span class="lineno"> 458</span> <span class="preprocessor">#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00459"></a><span class="lineno"> 459</span> </div><div class="line"><a name="l00460"></a><span class="lineno"> 460</span>  vstore8((<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>, 8))(row00.s0123, row01,</div><div class="line"><a name="l00461"></a><span class="lineno"> 461</span>  row10.s012),</div><div class="line"><a name="l00462"></a><span class="lineno"> 462</span>  0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00463"></a><span class="lineno"> 463</span>  vstore2((<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>, 2))(row10.s3, row11), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00464"></a><span class="lineno"> 464</span> </div><div class="line"><a name="l00465"></a><span class="lineno"> 465</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00466"></a><span class="lineno"> 466</span>  output_ptr += 10 * dst_stride_x;</div><div class="line"><a name="l00467"></a><span class="lineno"> 467</span>  }</div><div class="line"><a name="l00468"></a><span class="lineno"> 468</span> </div><div class="line"><a name="l00469"></a><span class="lineno"> 469</span>  {</div><div class="line"><a name="l00470"></a><span class="lineno"> 470</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>, 4)</div><div class="line"><a name="l00471"></a><span class="lineno"> 471</span>  row00 = vload4(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr);</div><div class="line"><a name="l00472"></a><span class="lineno"> 472</span>  <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="line"><a name="l00473"></a><span class="lineno"> 473</span>  row01 = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr + 4);</div><div class="line"><a name="l00474"></a><span class="lineno"> 474</span> </div><div class="line"><a name="l00475"></a><span class="lineno"> 475</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00476"></a><span class="lineno"> 476</span> </div><div class="line"><a name="l00477"></a><span class="lineno"> 477</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>, 4)</div><div class="line"><a name="l00478"></a><span class="lineno"> 478</span>  row10 = vload4(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr);</div><div class="line"><a name="l00479"></a><span class="lineno"> 479</span>  <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="line"><a name="l00480"></a><span class="lineno"> 480</span>  row11 = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr + 4);</div><div class="line"><a name="l00481"></a><span class="lineno"> 481</span> </div><div class="line"><a name="l00482"></a><span class="lineno"> 482</span> <span class="preprocessor">#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00483"></a><span class="lineno"> 483</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00484"></a><span class="lineno"> 484</span>  cond00 = x0_condition && (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4))y0_condition.s2;</div><div class="line"><a name="l00485"></a><span class="lineno"> 485</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00486"></a><span class="lineno"> 486</span>  cond10 = x0_condition && (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4))y0_condition.s3;</div><div class="line"><a name="l00487"></a><span class="lineno"> 487</span>  COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y0_condition.s2);</div><div class="line"><a name="l00488"></a><span class="lineno"> 488</span>  COND_DATA_TYPE cond11 = (COND_DATA_TYPE)(x1_condition && y0_condition.s3);</div><div class="line"><a name="l00489"></a><span class="lineno"> 489</span> </div><div class="line"><a name="l00490"></a><span class="lineno"> 490</span>  <span class="comment">// Replace with 0 if the value is not valid</span></div><div class="line"><a name="l00491"></a><span class="lineno"> 491</span>  row00 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 4))PAD_VALUE, row00, cond00);</div><div class="line"><a name="l00492"></a><span class="lineno"> 492</span>  row10 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 4))PAD_VALUE, row10, cond10);</div><div class="line"><a name="l00493"></a><span class="lineno"> 493</span>  row01 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>)PAD_VALUE, row01, cond01);</div><div class="line"><a name="l00494"></a><span class="lineno"> 494</span>  row11 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>)PAD_VALUE, row11, cond11);</div><div class="line"><a name="l00495"></a><span class="lineno"> 495</span> <span class="preprocessor">#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00496"></a><span class="lineno"> 496</span> </div><div class="line"><a name="l00497"></a><span class="lineno"> 497</span>  vstore8((<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>, 8))(row00.s0123, row01,</div><div class="line"><a name="l00498"></a><span class="lineno"> 498</span>  row10.s012),</div><div class="line"><a name="l00499"></a><span class="lineno"> 499</span>  0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00500"></a><span class="lineno"> 500</span>  vstore2((<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>, 2))(row10.s3, row11), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00501"></a><span class="lineno"> 501</span> </div><div class="line"><a name="l00502"></a><span class="lineno"> 502</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00503"></a><span class="lineno"> 503</span>  output_ptr += 10 * dst_stride_x;</div><div class="line"><a name="l00504"></a><span class="lineno"> 504</span>  }</div><div class="line"><a name="l00505"></a><span class="lineno"> 505</span> </div><div class="line"><a name="l00506"></a><span class="lineno"> 506</span>  {</div><div class="line"><a name="l00507"></a><span class="lineno"> 507</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>, 4)</div><div class="line"><a name="l00508"></a><span class="lineno"> 508</span>  row00 = vload4(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr);</div><div class="line"><a name="l00509"></a><span class="lineno"> 509</span>  <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a></div><div class="line"><a name="l00510"></a><span class="lineno"> 510</span>  row01 = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)input_ptr + 4);</div><div class="line"><a name="l00511"></a><span class="lineno"> 511</span> </div><div class="line"><a name="l00512"></a><span class="lineno"> 512</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00513"></a><span class="lineno"> 513</span> </div><div class="line"><a name="l00514"></a><span class="lineno"> 514</span> <span class="preprocessor">#if PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00515"></a><span class="lineno"> 515</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4)</div><div class="line"><a name="l00516"></a><span class="lineno"> 516</span>  cond00 = x0_condition && (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, 4))y1_condition;</div><div class="line"><a name="l00517"></a><span class="lineno"> 517</span>  COND_DATA_TYPE cond01 = (COND_DATA_TYPE)(x1_condition && y1_condition);</div><div class="line"><a name="l00518"></a><span class="lineno"> 518</span> </div><div class="line"><a name="l00519"></a><span class="lineno"> 519</span>  <span class="comment">// Replace with 0 if the value is not valid</span></div><div class="line"><a name="l00520"></a><span class="lineno"> 520</span>  row00 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<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>, 4))PAD_VALUE, row00, cond00);</div><div class="line"><a name="l00521"></a><span class="lineno"> 521</span>  row01 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>((<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>)PAD_VALUE, row01, cond01);</div><div class="line"><a name="l00522"></a><span class="lineno"> 522</span> <span class="preprocessor">#endif // PAD_LEFT != 0 || PAD_TOP != 0 || PAD_RIGHT != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00523"></a><span class="lineno"> 523</span> </div><div class="line"><a name="l00524"></a><span class="lineno"> 524</span>  vstore4(row00, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00525"></a><span class="lineno"> 525</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 4) = row01;</div><div class="line"><a name="l00526"></a><span class="lineno"> 526</span> </div><div class="line"><a name="l00527"></a><span class="lineno"> 527</span>  output_ptr += 5 * dst_stride_x;</div><div class="line"><a name="l00528"></a><span class="lineno"> 528</span>  }</div><div class="line"><a name="l00529"></a><span class="lineno"> 529</span> </div><div class="line"><a name="l00530"></a><span class="lineno"> 530</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00531"></a><span class="lineno"> 531</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00532"></a><span class="lineno"> 532</span>  <span class="keywordflow">if</span>((xo / 25) == (SRC_DEPTH / NUM_GROUPS - 1))</div><div class="line"><a name="l00533"></a><span class="lineno"> 533</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00534"></a><span class="lineno"> 534</span>  <span class="keywordflow">if</span>(ch == (SRC_DEPTH - 1))</div><div class="line"><a name="l00535"></a><span class="lineno"> 535</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00536"></a><span class="lineno"> 536</span>  {</div><div class="line"><a name="l00537"></a><span class="lineno"> 537</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr) = 1.0f;</div><div class="line"><a name="l00538"></a><span class="lineno"> 538</span>  }</div><div class="line"><a name="l00539"></a><span class="lineno"> 539</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l00540"></a><span class="lineno"> 540</span> }</div><div class="line"><a name="l00541"></a><span class="lineno"> 541</span> <span class="preprocessor">#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE)</span></div><div class="line"><a name="l00542"></a><span class="lineno"> 542</span> </div><div class="line"><a name="l00543"></a><span class="lineno"> 543</span> <span class="preprocessor">#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)</span></div><div class="line"><a name="l00544"></a><span class="lineno"> 544</span> <span class="comment">/** This opencl kernel performs im2col when the kernel size is 11x11, we do not have paddings and the data layout is NCHW</span></div><div class="line"><a name="l00545"></a><span class="lineno"> 545</span> <span class="comment"> *</span></div><div class="line"><a name="l00546"></a><span class="lineno"> 546</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00547"></a><span class="lineno"> 547</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l00548"></a><span class="lineno"> 548</span> <span class="comment"> * @note The number of input channels must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3</span></div><div class="line"><a name="l00549"></a><span class="lineno"> 549</span> <span class="comment"> * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1</span></div><div class="line"><a name="l00550"></a><span class="lineno"> 550</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00551"></a><span class="lineno"> 551</span> <span class="comment"> * @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4</span></div><div class="line"><a name="l00552"></a><span class="lineno"> 552</span> <span class="comment"> *</span></div><div class="line"><a name="l00553"></a><span class="lineno"> 553</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l00554"></a><span class="lineno"> 554</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="l00555"></a><span class="lineno"> 555</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="l00556"></a><span class="lineno"> 556</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="l00557"></a><span class="lineno"> 557</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="l00558"></a><span class="lineno"> 558</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="l00559"></a><span class="lineno"> 559</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00560"></a><span class="lineno"> 560</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="l00561"></a><span class="lineno"> 561</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="l00562"></a><span class="lineno"> 562</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="l00563"></a><span class="lineno"> 563</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="l00564"></a><span class="lineno"> 564</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="l00565"></a><span class="lineno"> 565</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="l00566"></a><span class="lineno"> 566</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="l00567"></a><span class="lineno"> 567</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00568"></a><span class="lineno"> 568</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="l00569"></a><span class="lineno"> 569</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="l00570"></a><span class="lineno"> 570</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="l00571"></a><span class="lineno"> 571</span> <span class="comment"> */</span></div><div class="line"><a name="l00572"></a><span class="lineno"> 572</span> __kernel <span class="keywordtype">void</span> im2col11x11_padx0_pady0_nchw(</div><div class="line"><a name="l00573"></a><span class="lineno"> 573</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="l00574"></a><span class="lineno"> 574</span> #<span class="keywordflow">if</span> defined(NUM_GROUPS)</div><div class="line"><a name="l00575"></a><span class="lineno"> 575</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="l00576"></a><span class="lineno"> 576</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00577"></a><span class="lineno"> 577</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="l00578"></a><span class="lineno"> 578</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00579"></a><span class="lineno"> 579</span>  uint src_stride_w,</div><div class="line"><a name="l00580"></a><span class="lineno"> 580</span>  uint dst_stride_w)</div><div class="line"><a name="l00581"></a><span class="lineno"> 581</span> {</div><div class="line"><a name="l00582"></a><span class="lineno"> 582</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xc = get_global_id(0); <span class="comment">// x coordinate in the convolved tensor</span></div><div class="line"><a name="l00583"></a><span class="lineno"> 583</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yc = get_global_id(1); <span class="comment">// y coordinate in the convolved tensor</span></div><div class="line"><a name="l00584"></a><span class="lineno"> 584</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = get_global_id(2) % SRC_DEPTH; <span class="comment">// input feature map</span></div><div class="line"><a name="l00585"></a><span class="lineno"> 585</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / SRC_DEPTH; <span class="comment">// batch size</span></div><div class="line"><a name="l00586"></a><span class="lineno"> 586</span> </div><div class="line"><a name="l00587"></a><span class="lineno"> 587</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l00588"></a><span class="lineno"> 588</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = xc * STRIDE_X;</div><div class="line"><a name="l00589"></a><span class="lineno"> 589</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = yc * STRIDE_Y;</div><div class="line"><a name="l00590"></a><span class="lineno"> 590</span> </div><div class="line"><a name="l00591"></a><span class="lineno"> 591</span>  <span class="comment">// Calculate output indices</span></div><div class="line"><a name="l00592"></a><span class="lineno"> 592</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00593"></a><span class="lineno"> 593</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * 121; <span class="comment">// 11x11</span></div><div class="line"><a name="l00594"></a><span class="lineno"> 594</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> zo = ch / (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00595"></a><span class="lineno"> 595</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00596"></a><span class="lineno"> 596</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = ch * 121; <span class="comment">// 11x11</span></div><div class="line"><a name="l00597"></a><span class="lineno"> 597</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00598"></a><span class="lineno"> 598</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = xc + yc * CONVOLVED_WIDTH; <span class="comment">// Index of the convolution</span></div><div class="line"><a name="l00599"></a><span class="lineno"> 599</span> </div><div class="line"><a name="l00600"></a><span class="lineno"> 600</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l00601"></a><span class="lineno"> 601</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + xi * src_stride_x + yi * src_stride_y + ch * src_stride_z + batch * src_stride_w;</div><div class="line"><a name="l00602"></a><span class="lineno"> 602</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00603"></a><span class="lineno"> 603</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w;</div><div class="line"><a name="l00604"></a><span class="lineno"> 604</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00605"></a><span class="lineno"> 605</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + xo * dst_stride_x + yo * dst_stride_y + batch * dst_stride_w;</div><div class="line"><a name="l00606"></a><span class="lineno"> 606</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00607"></a><span class="lineno"> 607</span> </div><div class="line"><a name="l00608"></a><span class="lineno"> 608</span>  {</div><div class="line"><a name="l00609"></a><span class="lineno"> 609</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>, 8)</div><div class="line"><a name="l00610"></a><span class="lineno"> 610</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00611"></a><span class="lineno"> 611</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>, 3)</div><div class="line"><a name="l00612"></a><span class="lineno"> 612</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00613"></a><span class="lineno"> 613</span> </div><div class="line"><a name="l00614"></a><span class="lineno"> 614</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00615"></a><span class="lineno"> 615</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00616"></a><span class="lineno"> 616</span> </div><div class="line"><a name="l00617"></a><span class="lineno"> 617</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00618"></a><span class="lineno"> 618</span>  output_ptr += 11 * src_stride_x;</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> </div><div class="line"><a name="l00621"></a><span class="lineno"> 621</span>  {</div><div class="line"><a name="l00622"></a><span class="lineno"> 622</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>, 8)</div><div class="line"><a name="l00623"></a><span class="lineno"> 623</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00624"></a><span class="lineno"> 624</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>, 3)</div><div class="line"><a name="l00625"></a><span class="lineno"> 625</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00626"></a><span class="lineno"> 626</span> </div><div class="line"><a name="l00627"></a><span class="lineno"> 627</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00628"></a><span class="lineno"> 628</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00629"></a><span class="lineno"> 629</span> </div><div class="line"><a name="l00630"></a><span class="lineno"> 630</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00631"></a><span class="lineno"> 631</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00632"></a><span class="lineno"> 632</span>  }</div><div class="line"><a name="l00633"></a><span class="lineno"> 633</span> </div><div class="line"><a name="l00634"></a><span class="lineno"> 634</span>  {</div><div class="line"><a name="l00635"></a><span class="lineno"> 635</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>, 8)</div><div class="line"><a name="l00636"></a><span class="lineno"> 636</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00637"></a><span class="lineno"> 637</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>, 3)</div><div class="line"><a name="l00638"></a><span class="lineno"> 638</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</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>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00641"></a><span class="lineno"> 641</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00642"></a><span class="lineno"> 642</span> </div><div class="line"><a name="l00643"></a><span class="lineno"> 643</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00644"></a><span class="lineno"> 644</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00645"></a><span class="lineno"> 645</span>  }</div><div class="line"><a name="l00646"></a><span class="lineno"> 646</span> </div><div class="line"><a name="l00647"></a><span class="lineno"> 647</span>  {</div><div class="line"><a name="l00648"></a><span class="lineno"> 648</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>, 8)</div><div class="line"><a name="l00649"></a><span class="lineno"> 649</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00650"></a><span class="lineno"> 650</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>, 3)</div><div class="line"><a name="l00651"></a><span class="lineno"> 651</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00652"></a><span class="lineno"> 652</span> </div><div class="line"><a name="l00653"></a><span class="lineno"> 653</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00654"></a><span class="lineno"> 654</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00655"></a><span class="lineno"> 655</span> </div><div class="line"><a name="l00656"></a><span class="lineno"> 656</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00657"></a><span class="lineno"> 657</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00658"></a><span class="lineno"> 658</span>  }</div><div class="line"><a name="l00659"></a><span class="lineno"> 659</span> </div><div class="line"><a name="l00660"></a><span class="lineno"> 660</span>  {</div><div class="line"><a name="l00661"></a><span class="lineno"> 661</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>, 8)</div><div class="line"><a name="l00662"></a><span class="lineno"> 662</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00663"></a><span class="lineno"> 663</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>, 3)</div><div class="line"><a name="l00664"></a><span class="lineno"> 664</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</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>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00667"></a><span class="lineno"> 667</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00668"></a><span class="lineno"> 668</span> </div><div class="line"><a name="l00669"></a><span class="lineno"> 669</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00670"></a><span class="lineno"> 670</span>  output_ptr += 11 * src_stride_x;</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> </div><div class="line"><a name="l00673"></a><span class="lineno"> 673</span>  {</div><div class="line"><a name="l00674"></a><span class="lineno"> 674</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>, 8)</div><div class="line"><a name="l00675"></a><span class="lineno"> 675</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00676"></a><span class="lineno"> 676</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>, 3)</div><div class="line"><a name="l00677"></a><span class="lineno"> 677</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00678"></a><span class="lineno"> 678</span> </div><div class="line"><a name="l00679"></a><span class="lineno"> 679</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00680"></a><span class="lineno"> 680</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00681"></a><span class="lineno"> 681</span> </div><div class="line"><a name="l00682"></a><span class="lineno"> 682</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00683"></a><span class="lineno"> 683</span>  output_ptr += 11 * src_stride_x;</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> </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>  <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>, 8)</div><div class="line"><a name="l00688"></a><span class="lineno"> 688</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00689"></a><span class="lineno"> 689</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>, 3)</div><div class="line"><a name="l00690"></a><span class="lineno"> 690</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00691"></a><span class="lineno"> 691</span> </div><div class="line"><a name="l00692"></a><span class="lineno"> 692</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00693"></a><span class="lineno"> 693</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00694"></a><span class="lineno"> 694</span> </div><div class="line"><a name="l00695"></a><span class="lineno"> 695</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00696"></a><span class="lineno"> 696</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00697"></a><span class="lineno"> 697</span>  }</div><div class="line"><a name="l00698"></a><span class="lineno"> 698</span> </div><div class="line"><a name="l00699"></a><span class="lineno"> 699</span>  {</div><div class="line"><a name="l00700"></a><span class="lineno"> 700</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>, 8)</div><div class="line"><a name="l00701"></a><span class="lineno"> 701</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00702"></a><span class="lineno"> 702</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>, 3)</div><div class="line"><a name="l00703"></a><span class="lineno"> 703</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00704"></a><span class="lineno"> 704</span> </div><div class="line"><a name="l00705"></a><span class="lineno"> 705</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00706"></a><span class="lineno"> 706</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00707"></a><span class="lineno"> 707</span> </div><div class="line"><a name="l00708"></a><span class="lineno"> 708</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00709"></a><span class="lineno"> 709</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00710"></a><span class="lineno"> 710</span>  }</div><div class="line"><a name="l00711"></a><span class="lineno"> 711</span> </div><div class="line"><a name="l00712"></a><span class="lineno"> 712</span>  {</div><div class="line"><a name="l00713"></a><span class="lineno"> 713</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>, 8)</div><div class="line"><a name="l00714"></a><span class="lineno"> 714</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00715"></a><span class="lineno"> 715</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>, 3)</div><div class="line"><a name="l00716"></a><span class="lineno"> 716</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00717"></a><span class="lineno"> 717</span> </div><div class="line"><a name="l00718"></a><span class="lineno"> 718</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00719"></a><span class="lineno"> 719</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00720"></a><span class="lineno"> 720</span> </div><div class="line"><a name="l00721"></a><span class="lineno"> 721</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00722"></a><span class="lineno"> 722</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00723"></a><span class="lineno"> 723</span>  }</div><div class="line"><a name="l00724"></a><span class="lineno"> 724</span> </div><div class="line"><a name="l00725"></a><span class="lineno"> 725</span>  {</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>, 8)</div><div class="line"><a name="l00727"></a><span class="lineno"> 727</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</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>, 3)</div><div class="line"><a name="l00729"></a><span class="lineno"> 729</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00730"></a><span class="lineno"> 730</span> </div><div class="line"><a name="l00731"></a><span class="lineno"> 731</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00732"></a><span class="lineno"> 732</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00733"></a><span class="lineno"> 733</span> </div><div class="line"><a name="l00734"></a><span class="lineno"> 734</span>  input_ptr += src_stride_y;</div><div class="line"><a name="l00735"></a><span class="lineno"> 735</span>  output_ptr += 11 * src_stride_x;</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> </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>  <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>, 8)</div><div class="line"><a name="l00740"></a><span class="lineno"> 740</span>  row00 = vload8(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr));</div><div class="line"><a name="l00741"></a><span class="lineno"> 741</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>, 3)</div><div class="line"><a name="l00742"></a><span class="lineno"> 742</span>  row01 = vload3(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr) + 8);</div><div class="line"><a name="l00743"></a><span class="lineno"> 743</span> </div><div class="line"><a name="l00744"></a><span class="lineno"> 744</span>  vstore8((<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>, 8))(row00.s01234567), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr);</div><div class="line"><a name="l00745"></a><span class="lineno"> 745</span>  vstore3((<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>, 3))(row01.s012), 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr + 8);</div><div class="line"><a name="l00746"></a><span class="lineno"> 746</span> </div><div class="line"><a name="l00747"></a><span class="lineno"> 747</span>  output_ptr += 11 * src_stride_x;</div><div class="line"><a name="l00748"></a><span class="lineno"> 748</span>  }</div><div class="line"><a name="l00749"></a><span class="lineno"> 749</span> </div><div class="line"><a name="l00750"></a><span class="lineno"> 750</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00751"></a><span class="lineno"> 751</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00752"></a><span class="lineno"> 752</span>  <span class="keywordflow">if</span>((xo / 121) == (SRC_DEPTH / NUM_GROUPS - 1))</div><div class="line"><a name="l00753"></a><span class="lineno"> 753</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00754"></a><span class="lineno"> 754</span>  <span class="keywordflow">if</span>(ch == (SRC_DEPTH - 1))</div><div class="line"><a name="l00755"></a><span class="lineno"> 755</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00756"></a><span class="lineno"> 756</span>  {</div><div class="line"><a name="l00757"></a><span class="lineno"> 757</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)output_ptr) = 1.0f;</div><div class="line"><a name="l00758"></a><span class="lineno"> 758</span>  }</div><div class="line"><a name="l00759"></a><span class="lineno"> 759</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l00760"></a><span class="lineno"> 760</span> }</div><div class="line"><a name="l00761"></a><span class="lineno"> 761</span> <span class="preprocessor">#endif // defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(SRC_DEPTH)</span></div><div class="line"><a name="l00762"></a><span class="lineno"> 762</span> </div><div class="line"><a name="l00763"></a><span class="lineno"> 763</span> <span class="preprocessor">#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)</span></div><div class="line"><a name="l00764"></a><span class="lineno"> 764</span> <span class="comment">/** This opencl kernel performs im2col when the kernel size is greater than 1x1, we do not have paddings and the data layout is NCHW</span></div><div class="line"><a name="l00765"></a><span class="lineno"> 765</span> <span class="comment"> *</span></div><div class="line"><a name="l00766"></a><span class="lineno"> 766</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float.</span></div><div class="line"><a name="l00767"></a><span class="lineno"> 767</span> <span class="comment"> * @note The vector size must be passed at compile time using -DVECTOR_SIZE e.g. -DVECTOR_SIZE=4.</span></div><div class="line"><a name="l00768"></a><span class="lineno"> 768</span> <span class="comment"> * @note The width modulo vector size must be passed at compile time using -DWIDTH_MOD_VECTOR_SIZE e.g. -DWIDTH_MOD_VECTOR_SIZE=3.</span></div><div class="line"><a name="l00769"></a><span class="lineno"> 769</span> <span class="comment"> * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1</span></div><div class="line"><a name="l00770"></a><span class="lineno"> 770</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00771"></a><span class="lineno"> 771</span> <span class="comment"> * @note In case grouping is performed, the number of groups must be passed at compile time using -DNUM_GROUPS: e.g. -DNUM_GROUPS=4</span></div><div class="line"><a name="l00772"></a><span class="lineno"> 772</span> <span class="comment"> *</span></div><div class="line"><a name="l00773"></a><span class="lineno"> 773</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="l00774"></a><span class="lineno"> 774</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="l00775"></a><span class="lineno"> 775</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="l00776"></a><span class="lineno"> 776</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="l00777"></a><span class="lineno"> 777</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="l00778"></a><span class="lineno"> 778</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="l00779"></a><span class="lineno"> 779</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00780"></a><span class="lineno"> 780</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="l00781"></a><span class="lineno"> 781</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="l00782"></a><span class="lineno"> 782</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="l00783"></a><span class="lineno"> 783</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="l00784"></a><span class="lineno"> 784</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="l00785"></a><span class="lineno"> 785</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="l00786"></a><span class="lineno"> 786</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="l00787"></a><span class="lineno"> 787</span> <span class="comment"> * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00788"></a><span class="lineno"> 788</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="l00789"></a><span class="lineno"> 789</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="l00790"></a><span class="lineno"> 790</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="l00791"></a><span class="lineno"> 791</span> <span class="comment"> */</span></div><div class="line"><a name="l00792"></a><span class="lineno"> 792</span> __kernel <span class="keywordtype">void</span> im2col_generic_padx0_pady0_nchw(</div><div class="line"><a name="l00793"></a><span class="lineno"> 793</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="l00794"></a><span class="lineno"> 794</span> #<span class="keywordflow">if</span> defined(NUM_GROUPS)</div><div class="line"><a name="l00795"></a><span class="lineno"> 795</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="l00796"></a><span class="lineno"> 796</span> #<span class="keywordflow">else</span> <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00797"></a><span class="lineno"> 797</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="l00798"></a><span class="lineno"> 798</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00799"></a><span class="lineno"> 799</span>  uint src_stride_w,</div><div class="line"><a name="l00800"></a><span class="lineno"> 800</span>  uint dst_stride_w)</div><div class="line"><a name="l00801"></a><span class="lineno"> 801</span> {</div><div class="line"><a name="l00802"></a><span class="lineno"> 802</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xc = get_global_id(0); <span class="comment">// x coordinate in the convolved tensor</span></div><div class="line"><a name="l00803"></a><span class="lineno"> 803</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yc = get_global_id(1); <span class="comment">// y coordinate in the convolved tensor</span></div><div class="line"><a name="l00804"></a><span class="lineno"> 804</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = get_global_id(2) % SRC_DEPTH; <span class="comment">// input feature map</span></div><div class="line"><a name="l00805"></a><span class="lineno"> 805</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2) / SRC_DEPTH; <span class="comment">// batch size</span></div><div class="line"><a name="l00806"></a><span class="lineno"> 806</span> </div><div class="line"><a name="l00807"></a><span class="lineno"> 807</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l00808"></a><span class="lineno"> 808</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = xc * STRIDE_X;</div><div class="line"><a name="l00809"></a><span class="lineno"> 809</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = yc * 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">// Calculate output indices</span></div><div class="line"><a name="l00812"></a><span class="lineno"> 812</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00813"></a><span class="lineno"> 813</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = (ch % (SRC_DEPTH / NUM_GROUPS)) * KERNEL_WIDTH * KERNEL_HEIGHT;</div><div class="line"><a name="l00814"></a><span class="lineno"> 814</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> zo = ch / (SRC_DEPTH / NUM_GROUPS);</div><div class="line"><a name="l00815"></a><span class="lineno"> 815</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00816"></a><span class="lineno"> 816</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xo = ch * KERNEL_WIDTH * KERNEL_HEIGHT;</div><div class="line"><a name="l00817"></a><span class="lineno"> 817</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00818"></a><span class="lineno"> 818</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = xc + yc * CONVOLVED_WIDTH; <span class="comment">// Index of the convolution</span></div><div class="line"><a name="l00819"></a><span class="lineno"> 819</span> </div><div class="line"><a name="l00820"></a><span class="lineno"> 820</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * src_stride_z + batch * src_stride_w;</div><div class="line"><a name="l00821"></a><span class="lineno"> 821</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00822"></a><span class="lineno"> 822</span>  __global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *output_ptr = ((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + zo * dst_stride_z + batch * dst_stride_w)) + xo;</div><div class="line"><a name="l00823"></a><span class="lineno"> 823</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00824"></a><span class="lineno"> 824</span>  __global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *output_ptr = ((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(dst_ptr + dst_offset_first_element_in_bytes + yo * dst_stride_y + batch * dst_stride_w)) + xo;</div><div class="line"><a name="l00825"></a><span class="lineno"> 825</span> <span class="preprocessor">#endif // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00826"></a><span class="lineno"> 826</span> </div><div class="line"><a name="l00827"></a><span class="lineno"> 827</span>  <span class="comment">// Linearize convolution elements</span></div><div class="line"><a name="l00828"></a><span class="lineno"> 828</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> y = yi, y_e = yi + KERNEL_HEIGHT; y < y_e; ++y)</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>  <span class="keywordtype">int</span> last_x = 0;</div><div class="line"><a name="l00831"></a><span class="lineno"> 831</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> x = xi, x_e = xi + KERNEL_WIDTH; x + <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a> <= x_e; x += <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>, output_ptr += <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l00832"></a><span class="lineno"> 832</span>  {</div><div class="line"><a name="l00833"></a><span class="lineno"> 833</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="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l00834"></a><span class="lineno"> 834</span>  row = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + x * src_stride_x + y * src_stride_y));</div><div class="line"><a name="l00835"></a><span class="lineno"> 835</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l00836"></a><span class="lineno"> 836</span>  (row, 0, output_ptr);</div><div class="line"><a name="l00837"></a><span class="lineno"> 837</span>  last_x = x;</div><div class="line"><a name="l00838"></a><span class="lineno"> 838</span>  }</div><div class="line"><a name="l00839"></a><span class="lineno"> 839</span>  <span class="comment">// Copy the remainder of the row by doing VLOAD(WIDTH_MOD_VECTOR_SIZE) and VSTORE(WIDTH_MOD_VECTOR_SIZE).</span></div><div class="line"><a name="l00840"></a><span class="lineno"> 840</span>  <span class="comment">// Note that x and output_ptr have already been incremented by VECTOR_SIZE by the loop just before exit.</span></div><div class="line"><a name="l00841"></a><span class="lineno"> 841</span> <span class="preprocessor">#if WIDTH_MOD_VECTOR_SIZE == 1</span></div><div class="line"><a name="l00842"></a><span class="lineno"> 842</span>  *output_ptr = *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + (last_x + <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>) * src_stride_x + y * src_stride_y));</div><div class="line"><a name="l00843"></a><span class="lineno"> 843</span> <span class="preprocessor">#elif WIDTH_MOD_VECTOR_SIZE > 1</span></div><div class="line"><a name="l00844"></a><span class="lineno"> 844</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>, WIDTH_MOD_VECTOR_SIZE)</div><div class="line"><a name="l00845"></a><span class="lineno"> 845</span>  row = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(WIDTH_MOD_VECTOR_SIZE)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + (last_x + <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>) * src_stride_x + y * src_stride_y));</div><div class="line"><a name="l00846"></a><span class="lineno"> 846</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(WIDTH_MOD_VECTOR_SIZE)</div><div class="line"><a name="l00847"></a><span class="lineno"> 847</span>  (row, 0, output_ptr);</div><div class="line"><a name="l00848"></a><span class="lineno"> 848</span> <span class="preprocessor">#endif </span><span class="comment">/* WIDTH_MOD_VECTOR_SIZE */</span><span class="preprocessor"></span></div><div class="line"><a name="l00849"></a><span class="lineno"> 849</span>  output_ptr += WIDTH_MOD_VECTOR_SIZE;</div><div class="line"><a name="l00850"></a><span class="lineno"> 850</span>  } <span class="comment">/* End of loop over KERNEL_HEIGHT */</span></div><div class="line"><a name="l00851"></a><span class="lineno"> 851</span> </div><div class="line"><a name="l00852"></a><span class="lineno"> 852</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l00853"></a><span class="lineno"> 853</span> <span class="preprocessor">#if defined(NUM_GROUPS)</span></div><div class="line"><a name="l00854"></a><span class="lineno"> 854</span>  <span class="keywordflow">if</span>((xo / (KERNEL_WIDTH * KERNEL_HEIGHT)) == (SRC_DEPTH / NUM_GROUPS - 1))</div><div class="line"><a name="l00855"></a><span class="lineno"> 855</span> <span class="preprocessor">#else // defined(NUM_GROUPS)</span></div><div class="line"><a name="l00856"></a><span class="lineno"> 856</span>  <span class="keywordflow">if</span>(ch == (SRC_DEPTH - 1))</div><div class="line"><a name="l00857"></a><span class="lineno"> 857</span> #endif <span class="comment">// defined(NUM_GROUPS)</span></div><div class="line"><a name="l00858"></a><span class="lineno"> 858</span>  {</div><div class="line"><a name="l00859"></a><span class="lineno"> 859</span>  *output_ptr = 1.0f;</div><div class="line"><a name="l00860"></a><span class="lineno"> 860</span>  }</div><div class="line"><a name="l00861"></a><span class="lineno"> 861</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l00862"></a><span class="lineno"> 862</span> }</div><div class="line"><a name="l00863"></a><span class="lineno"> 863</span> <span class="preprocessor">#endif //defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(VECTOR_SIZE) && defined(WIDTH_MOD_VECTOR_SIZE)</span></div><div class="line"><a name="l00864"></a><span class="lineno"> 864</span> </div><div class="line"><a name="l00865"></a><span class="lineno"> 865</span> <span class="preprocessor">#if defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)</span></div><div class="line"><a name="l00866"></a><span class="lineno"> 866</span> </div><div class="line"><a name="l00867"></a><span class="lineno"> 867</span> <span class="preprocessor">#define VECTOR_N VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)</span></div><div class="line"><a name="l00868"></a><span class="lineno"> 868</span> <span class="comment"></span></div><div class="line"><a name="l00869"></a><span class="lineno"> 869</span> <span class="comment">/** This kernel performs im2col when the kernel size is 3x3 and the data layout is NHWC</span></div><div class="line"><a name="l00870"></a><span class="lineno"> 870</span> <span class="comment"> *</span></div><div class="line"><a name="l00871"></a><span class="lineno"> 871</span> <span class="comment"> * @note This kernel computes VECTOR_SIZE elements</span></div><div class="line"><a name="l00872"></a><span class="lineno"> 872</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l00873"></a><span class="lineno"> 873</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l00874"></a><span class="lineno"> 874</span> <span class="comment"> * @note The kernel depth must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3</span></div><div class="line"><a name="l00875"></a><span class="lineno"> 875</span> <span class="comment"> * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1</span></div><div class="line"><a name="l00876"></a><span class="lineno"> 876</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l00877"></a><span class="lineno"> 877</span> <span class="comment"> *</span></div><div class="line"><a name="l00878"></a><span class="lineno"> 878</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l00879"></a><span class="lineno"> 879</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="l00880"></a><span class="lineno"> 880</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="l00881"></a><span class="lineno"> 881</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="l00882"></a><span class="lineno"> 882</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="l00883"></a><span class="lineno"> 883</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="l00884"></a><span class="lineno"> 884</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l00885"></a><span class="lineno"> 885</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="l00886"></a><span class="lineno"> 886</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="l00887"></a><span class="lineno"> 887</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="l00888"></a><span class="lineno"> 888</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="l00889"></a><span class="lineno"> 889</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="l00890"></a><span class="lineno"> 890</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="l00891"></a><span class="lineno"> 891</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="l00892"></a><span class="lineno"> 892</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="l00893"></a><span class="lineno"> 893</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="l00894"></a><span class="lineno"> 894</span> <span class="comment"> */</span></div><div class="line"><a name="l00895"></a><span class="lineno"> 895</span> __kernel <span class="keywordtype">void</span> im2col3x3_nhwc(</div><div class="line"><a name="l00896"></a><span class="lineno"> 896</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="l00897"></a><span class="lineno"> 897</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="l00898"></a><span class="lineno"> 898</span>  uint src_stride_w,</div><div class="line"><a name="l00899"></a><span class="lineno"> 899</span>  uint dst_stride_w)</div><div class="line"><a name="l00900"></a><span class="lineno"> 900</span> {</div><div class="line"><a name="l00901"></a><span class="lineno"> 901</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = min((<span class="keywordtype">int</span>)(get_global_id(0) * <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>), LAST_ACCESSED); <span class="comment">// input feature map</span></div><div class="line"><a name="l00902"></a><span class="lineno"> 902</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = get_global_id(1);</div><div class="line"><a name="l00903"></a><span class="lineno"> 903</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2); <span class="comment">// batch size</span></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="comment">// Calculate input indices</span></div><div class="line"><a name="l00906"></a><span class="lineno"> 906</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;</div><div class="line"><a name="l00907"></a><span class="lineno"> 907</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;</div><div class="line"><a name="l00908"></a><span class="lineno"> 908</span> </div><div class="line"><a name="l00909"></a><span class="lineno"> 909</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l00910"></a><span class="lineno"> 910</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) + batch * (<span class="keywordtype">int</span>)src_stride_w;</div><div class="line"><a name="l00911"></a><span class="lineno"> 911</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) + yo * (<span class="keywordtype">int</span>)dst_stride_y + batch * (int)dst_stride_w;</div><div class="line"><a name="l00912"></a><span class="lineno"> 912</span> </div><div class="line"><a name="l00913"></a><span class="lineno"> 913</span>  <span class="keywordtype">int</span> yi_coord = 0;</div><div class="line"><a name="l00914"></a><span class="lineno"> 914</span>  int3 <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="l00915"></a><span class="lineno"> 915</span> </div><div class="line"><a name="l00916"></a><span class="lineno"> 916</span>  <span class="comment">// Clamp xi</span></div><div class="line"><a name="l00917"></a><span class="lineno"> 917</span>  int3 xi_offset = ((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT);</div><div class="line"><a name="l00918"></a><span class="lineno"> 918</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00919"></a><span class="lineno"> 919</span> <span class="preprocessor">#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)</span></div><div class="line"><a name="l00920"></a><span class="lineno"> 920</span>  xi_offset = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aabdbe431f2713c5c2604cb9872b66aab">CLAMP</a>(xi_offset, (int3)0, (int3)(SRC_WIDTH - 1));</div><div class="line"><a name="l00921"></a><span class="lineno"> 921</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00922"></a><span class="lineno"> 922</span>  xi_offset *= (int3)src_stride_y;</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>  <span class="comment">// Out-of-bound condition for X</span></div><div class="line"><a name="l00925"></a><span class="lineno"> 925</span>  int3 x_cond = (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) < (int3)0) || (((int3)xi + (int3)(0, 1, 2) * DILATION_X - (int3)PAD_LEFT) >= (int3)SRC_WIDTH);</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>  <span class="comment">// yi == 0</span></div><div class="line"><a name="l00928"></a><span class="lineno"> 928</span>  <span class="comment">// Clamp yi</span></div><div class="line"><a name="l00929"></a><span class="lineno"> 929</span>  <span class="comment">// yi_coord is casted to unsigned int in order to use just a min() operation</span></div><div class="line"><a name="l00930"></a><span class="lineno"> 930</span>  <span class="comment">// A "-1" 32 bit signed variable converted to unsigned gives 4294967295</span></div><div class="line"><a name="l00931"></a><span class="lineno"> 931</span>  yi_coord = yi - (int)PAD_TOP;</div><div class="line"><a name="l00932"></a><span class="lineno"> 932</span> </div><div class="line"><a name="l00933"></a><span class="lineno"> 933</span>  <span class="comment">// Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0</span></div><div class="line"><a name="l00934"></a><span class="lineno"> 934</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00935"></a><span class="lineno"> 935</span>  yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));</div><div class="line"><a name="l00936"></a><span class="lineno"> 936</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00937"></a><span class="lineno"> 937</span> </div><div class="line"><a name="l00938"></a><span class="lineno"> 938</span>  <span class="comment">// Compute offset</span></div><div class="line"><a name="l00939"></a><span class="lineno"> 939</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = xi_offset + (yi_coord * (int)src_stride_z);</div><div class="line"><a name="l00940"></a><span class="lineno"> 940</span> </div><div class="line"><a name="l00941"></a><span class="lineno"> 941</span>  <span class="comment">// Load input values</span></div><div class="line"><a name="l00942"></a><span class="lineno"> 942</span>  VECTOR_N values0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00943"></a><span class="lineno"> 943</span>  VECTOR_N values1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00944"></a><span class="lineno"> 944</span>  VECTOR_N values2 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00945"></a><span class="lineno"> 945</span> </div><div class="line"><a name="l00946"></a><span class="lineno"> 946</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l00947"></a><span class="lineno"> 947</span>  <span class="comment">// Replace invalid values with PAD_VALUE</span></div><div class="line"><a name="l00948"></a><span class="lineno"> 948</span>  <span class="keywordtype">int</span> y_cond = (int)((uint)(yi - (int)PAD_TOP) >= (uint)(SRC_HEIGHT));</div><div class="line"><a name="l00949"></a><span class="lineno"> 949</span>  values0 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values0, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s0));</div><div class="line"><a name="l00950"></a><span class="lineno"> 950</span>  values1 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values1, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s1));</div><div class="line"><a name="l00951"></a><span class="lineno"> 951</span>  values2 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values2, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s2));</div><div class="line"><a name="l00952"></a><span class="lineno"> 952</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l00953"></a><span class="lineno"> 953</span> </div><div class="line"><a name="l00954"></a><span class="lineno"> 954</span>  <span class="comment">// yi == 1</span></div><div class="line"><a name="l00955"></a><span class="lineno"> 955</span>  <span class="comment">// Clamp yi_coord (it can be negative if PAD_TOP > 1)</span></div><div class="line"><a name="l00956"></a><span class="lineno"> 956</span>  yi_coord = yi - (int)PAD_TOP + 1 * DILATION_Y;</div><div class="line"><a name="l00957"></a><span class="lineno"> 957</span> </div><div class="line"><a name="l00958"></a><span class="lineno"> 958</span>  <span class="comment">// Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0</span></div><div class="line"><a name="l00959"></a><span class="lineno"> 959</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00960"></a><span class="lineno"> 960</span>  yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));</div><div class="line"><a name="l00961"></a><span class="lineno"> 961</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00962"></a><span class="lineno"> 962</span> </div><div class="line"><a name="l00963"></a><span class="lineno"> 963</span>  <span class="comment">// Compute offset</span></div><div class="line"><a name="l00964"></a><span class="lineno"> 964</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = xi_offset + (yi_coord * (int)src_stride_z);</div><div class="line"><a name="l00965"></a><span class="lineno"> 965</span> </div><div class="line"><a name="l00966"></a><span class="lineno"> 966</span>  <span class="comment">// Load input values</span></div><div class="line"><a name="l00967"></a><span class="lineno"> 967</span>  VECTOR_N values3 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00968"></a><span class="lineno"> 968</span>  VECTOR_N values4 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00969"></a><span class="lineno"> 969</span>  VECTOR_N values5 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00970"></a><span class="lineno"> 970</span> </div><div class="line"><a name="l00971"></a><span class="lineno"> 971</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l00972"></a><span class="lineno"> 972</span>  <span class="comment">// Replace invalid values with zeros</span></div><div class="line"><a name="l00973"></a><span class="lineno"> 973</span>  y_cond = (int)((uint)(yi - (int)PAD_TOP + 1 * DILATION_Y) >= (uint)(SRC_HEIGHT));</div><div class="line"><a name="l00974"></a><span class="lineno"> 974</span>  values3 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values3, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s0));</div><div class="line"><a name="l00975"></a><span class="lineno"> 975</span>  values4 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values4, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s1));</div><div class="line"><a name="l00976"></a><span class="lineno"> 976</span>  values5 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values5, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s2));</div><div class="line"><a name="l00977"></a><span class="lineno"> 977</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l00978"></a><span class="lineno"> 978</span> </div><div class="line"><a name="l00979"></a><span class="lineno"> 979</span>  <span class="comment">// yi == 2</span></div><div class="line"><a name="l00980"></a><span class="lineno"> 980</span>  <span class="comment">// Clamp yi_coord</span></div><div class="line"><a name="l00981"></a><span class="lineno"> 981</span>  yi_coord = yi - (int)PAD_TOP + 2 * DILATION_Y;</div><div class="line"><a name="l00982"></a><span class="lineno"> 982</span> </div><div class="line"><a name="l00983"></a><span class="lineno"> 983</span>  <span class="comment">// Clamp only if PAD_TOP or PAD_BOTTOM is not equal to 0</span></div><div class="line"><a name="l00984"></a><span class="lineno"> 984</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00985"></a><span class="lineno"> 985</span>  yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1));</div><div class="line"><a name="l00986"></a><span class="lineno"> 986</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l00987"></a><span class="lineno"> 987</span> </div><div class="line"><a name="l00988"></a><span class="lineno"> 988</span>  <span class="comment">// Compute offset</span></div><div class="line"><a name="l00989"></a><span class="lineno"> 989</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = xi_offset + (yi_coord * (int)src_stride_z);</div><div class="line"><a name="l00990"></a><span class="lineno"> 990</span> </div><div class="line"><a name="l00991"></a><span class="lineno"> 991</span>  <span class="comment">// Load input values</span></div><div class="line"><a name="l00992"></a><span class="lineno"> 992</span>  VECTOR_N values6 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00993"></a><span class="lineno"> 993</span>  VECTOR_N values7 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00994"></a><span class="lineno"> 994</span>  VECTOR_N values8 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <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="l00995"></a><span class="lineno"> 995</span> </div><div class="line"><a name="l00996"></a><span class="lineno"> 996</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l00997"></a><span class="lineno"> 997</span>  <span class="comment">// Replace invalid values with PAD_VALUE</span></div><div class="line"><a name="l00998"></a><span class="lineno"> 998</span>  y_cond = (int)((uint)(yi - (int)PAD_TOP + 2 * DILATION_Y) >= (uint)(SRC_HEIGHT));</div><div class="line"><a name="l00999"></a><span class="lineno"> 999</span>  values6 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values6, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s0));</div><div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>  values7 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values7, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s1));</div><div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>  values8 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values8, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))y_cond || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(x_cond.s2));</div><div class="line"><a name="l01002"></a><span class="lineno"> 1002</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l01003"></a><span class="lineno"> 1003</span> </div><div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>  <span class="comment">// Store</span></div><div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>  (values0, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 0 * SRC_DEPTH);</div><div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>  (values1, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 1 * SRC_DEPTH);</div><div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>  (values2, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 2 * SRC_DEPTH);</div><div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>  (values3, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 3 * SRC_DEPTH);</div><div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>  (values4, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 4 * SRC_DEPTH);</div><div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>  (values5, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 5 * SRC_DEPTH);</div><div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>  (values6, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 6 * SRC_DEPTH);</div><div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>  (values7, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 7 * SRC_DEPTH);</div><div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01022"></a><span class="lineno"> 1022</span>  (values8, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + 8 * SRC_DEPTH);</div><div class="line"><a name="l01023"></a><span class="lineno"> 1023</span> </div><div class="line"><a name="l01024"></a><span class="lineno"> 1024</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01025"></a><span class="lineno"> 1025</span>  <span class="keywordflow">if</span>((ch + <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>) >= SRC_DEPTH)</div><div class="line"><a name="l01026"></a><span class="lineno"> 1026</span>  {</div><div class="line"><a name="l01027"></a><span class="lineno"> 1027</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) - ch + SRC_DEPTH * 9) = 1.0f;</div><div class="line"><a name="l01028"></a><span class="lineno"> 1028</span>  }</div><div class="line"><a name="l01029"></a><span class="lineno"> 1029</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l01030"></a><span class="lineno"> 1030</span> }</div><div class="line"><a name="l01031"></a><span class="lineno"> 1031</span> </div><div class="line"><a name="l01032"></a><span class="lineno"> 1032</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l01033"></a><span class="lineno"> 1033</span> <span class="preprocessor">#define IM2COL1x9(i) \</span></div><div class="line"><a name="l01034"></a><span class="lineno"> 1034</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l01035"></a><span class="lineno"> 1035</span> <span class="preprocessor"> yi_coord = yi - (int)PAD_TOP + i * DILATION_Y; \</span></div><div class="line"><a name="l01036"></a><span class="lineno"> 1036</span> <span class="preprocessor"> yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1)); \</span></div><div class="line"><a name="l01037"></a><span class="lineno"> 1037</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01038"></a><span class="lineno"> 1038</span> <span class="preprocessor"> offset0 = xi_offset0 + (yi_coord * (int)src_stride_z); \</span></div><div class="line"><a name="l01039"></a><span class="lineno"> 1039</span> <span class="preprocessor"> offset1 = xi_offset1 + (yi_coord * (int)src_stride_z); \</span></div><div class="line"><a name="l01040"></a><span class="lineno"> 1040</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01041"></a><span class="lineno"> 1041</span> <span class="preprocessor"> VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s0)); \</span></div><div class="line"><a name="l01042"></a><span class="lineno"> 1042</span> <span class="preprocessor"> VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s1)); \</span></div><div class="line"><a name="l01043"></a><span class="lineno"> 1043</span> <span class="preprocessor"> VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s2)); \</span></div><div class="line"><a name="l01044"></a><span class="lineno"> 1044</span> <span class="preprocessor"> VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s3)); \</span></div><div class="line"><a name="l01045"></a><span class="lineno"> 1045</span> <span class="preprocessor"> VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s4)); \</span></div><div class="line"><a name="l01046"></a><span class="lineno"> 1046</span> <span class="preprocessor"> VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s5)); \</span></div><div class="line"><a name="l01047"></a><span class="lineno"> 1047</span> <span class="preprocessor"> VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s6)); \</span></div><div class="line"><a name="l01048"></a><span class="lineno"> 1048</span> <span class="preprocessor"> VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s7)); \</span></div><div class="line"><a name="l01049"></a><span class="lineno"> 1049</span> <span class="preprocessor"> VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset1)); \</span></div><div class="line"><a name="l01050"></a><span class="lineno"> 1050</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01051"></a><span class="lineno"> 1051</span> <span class="preprocessor"> int y_cond = (int)((uint)(yi - (int)PAD_TOP + i * DILATION_Y) >= (uint)(SRC_HEIGHT)); \</span></div><div class="line"><a name="l01052"></a><span class="lineno"> 1052</span> <span class="preprocessor"> values0 = select(values0, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s0)); \</span></div><div class="line"><a name="l01053"></a><span class="lineno"> 1053</span> <span class="preprocessor"> values1 = select(values1, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s1)); \</span></div><div class="line"><a name="l01054"></a><span class="lineno"> 1054</span> <span class="preprocessor"> values2 = select(values2, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s2)); \</span></div><div class="line"><a name="l01055"></a><span class="lineno"> 1055</span> <span class="preprocessor"> values3 = select(values3, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s3)); \</span></div><div class="line"><a name="l01056"></a><span class="lineno"> 1056</span> <span class="preprocessor"> values4 = select(values4, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s4)); \</span></div><div class="line"><a name="l01057"></a><span class="lineno"> 1057</span> <span class="preprocessor"> values5 = select(values5, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s5)); \</span></div><div class="line"><a name="l01058"></a><span class="lineno"> 1058</span> <span class="preprocessor"> values6 = select(values6, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s6)); \</span></div><div class="line"><a name="l01059"></a><span class="lineno"> 1059</span> <span class="preprocessor"> values7 = select(values7, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond0.s7)); \</span></div><div class="line"><a name="l01060"></a><span class="lineno"> 1060</span> <span class="preprocessor"> values8 = select(values8, (VECTOR_N)PAD_VALUE, (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))y_cond || (VEC_DATA_TYPE(COND_DATA_TYPE, VECTOR_SIZE))(x_cond1)); \</span></div><div class="line"><a name="l01061"></a><span class="lineno"> 1061</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01062"></a><span class="lineno"> 1062</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01063"></a><span class="lineno"> 1063</span> <span class="preprocessor"> (values0, 0, (__global DATA_TYPE *)(output_ptr) + (0 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01064"></a><span class="lineno"> 1064</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01065"></a><span class="lineno"> 1065</span> <span class="preprocessor"> (values1, 0, (__global DATA_TYPE *)(output_ptr) + (1 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01066"></a><span class="lineno"> 1066</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01067"></a><span class="lineno"> 1067</span> <span class="preprocessor"> (values2, 0, (__global DATA_TYPE *)(output_ptr) + (2 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01068"></a><span class="lineno"> 1068</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01069"></a><span class="lineno"> 1069</span> <span class="preprocessor"> (values3, 0, (__global DATA_TYPE *)(output_ptr) + (3 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01070"></a><span class="lineno"> 1070</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01071"></a><span class="lineno"> 1071</span> <span class="preprocessor"> (values4, 0, (__global DATA_TYPE *)(output_ptr) + (4 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01072"></a><span class="lineno"> 1072</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01073"></a><span class="lineno"> 1073</span> <span class="preprocessor"> (values5, 0, (__global DATA_TYPE *)(output_ptr) + (5 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01074"></a><span class="lineno"> 1074</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01075"></a><span class="lineno"> 1075</span> <span class="preprocessor"> (values6, 0, (__global DATA_TYPE *)(output_ptr) + (6 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01076"></a><span class="lineno"> 1076</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01077"></a><span class="lineno"> 1077</span> <span class="preprocessor"> (values7, 0, (__global DATA_TYPE *)(output_ptr) + (7 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01078"></a><span class="lineno"> 1078</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01079"></a><span class="lineno"> 1079</span> <span class="preprocessor"> (values8, 0, (__global DATA_TYPE *)(output_ptr) + (8 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01080"></a><span class="lineno"> 1080</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l01081"></a><span class="lineno"> 1081</span> <span class="preprocessor">#else // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l01082"></a><span class="lineno"> 1082</span> <span class="preprocessor">#define IM2COL1x9(i) \</span></div><div class="line"><a name="l01083"></a><span class="lineno"> 1083</span> <span class="preprocessor"> ({ \</span></div><div class="line"><a name="l01084"></a><span class="lineno"> 1084</span> <span class="preprocessor"> yi_coord = yi - (int)PAD_TOP + i * DILATION_Y; \</span></div><div class="line"><a name="l01085"></a><span class="lineno"> 1085</span> <span class="preprocessor"> yi_coord = min((uint)yi_coord, (uint)(SRC_HEIGHT - 1)); \</span></div><div class="line"><a name="l01086"></a><span class="lineno"> 1086</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01087"></a><span class="lineno"> 1087</span> <span class="preprocessor"> offset0 = xi_offset0 + (yi_coord * (int)src_stride_z); \</span></div><div class="line"><a name="l01088"></a><span class="lineno"> 1088</span> <span class="preprocessor"> offset1 = xi_offset1 + (yi_coord * (int)src_stride_z); \</span></div><div class="line"><a name="l01089"></a><span class="lineno"> 1089</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01090"></a><span class="lineno"> 1090</span> <span class="preprocessor"> VECTOR_N values0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s0)); \</span></div><div class="line"><a name="l01091"></a><span class="lineno"> 1091</span> <span class="preprocessor"> VECTOR_N values1 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s1)); \</span></div><div class="line"><a name="l01092"></a><span class="lineno"> 1092</span> <span class="preprocessor"> VECTOR_N values2 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s2)); \</span></div><div class="line"><a name="l01093"></a><span class="lineno"> 1093</span> <span class="preprocessor"> VECTOR_N values3 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s3)); \</span></div><div class="line"><a name="l01094"></a><span class="lineno"> 1094</span> <span class="preprocessor"> VECTOR_N values4 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s4)); \</span></div><div class="line"><a name="l01095"></a><span class="lineno"> 1095</span> <span class="preprocessor"> VECTOR_N values5 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s5)); \</span></div><div class="line"><a name="l01096"></a><span class="lineno"> 1096</span> <span class="preprocessor"> VECTOR_N values6 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s6)); \</span></div><div class="line"><a name="l01097"></a><span class="lineno"> 1097</span> <span class="preprocessor"> VECTOR_N values7 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset0.s7)); \</span></div><div class="line"><a name="l01098"></a><span class="lineno"> 1098</span> <span class="preprocessor"> VECTOR_N values8 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(input_ptr + offset1)); \</span></div><div class="line"><a name="l01099"></a><span class="lineno"> 1099</span> <span class="preprocessor"> \</span></div><div class="line"><a name="l01100"></a><span class="lineno"> 1100</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01101"></a><span class="lineno"> 1101</span> <span class="preprocessor"> (values0, 0, (__global DATA_TYPE *)(output_ptr) + (0 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01102"></a><span class="lineno"> 1102</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01103"></a><span class="lineno"> 1103</span> <span class="preprocessor"> (values1, 0, (__global DATA_TYPE *)(output_ptr) + (1 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01104"></a><span class="lineno"> 1104</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01105"></a><span class="lineno"> 1105</span> <span class="preprocessor"> (values2, 0, (__global DATA_TYPE *)(output_ptr) + (2 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01106"></a><span class="lineno"> 1106</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01107"></a><span class="lineno"> 1107</span> <span class="preprocessor"> (values3, 0, (__global DATA_TYPE *)(output_ptr) + (3 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01108"></a><span class="lineno"> 1108</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01109"></a><span class="lineno"> 1109</span> <span class="preprocessor"> (values4, 0, (__global DATA_TYPE *)(output_ptr) + (4 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01110"></a><span class="lineno"> 1110</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01111"></a><span class="lineno"> 1111</span> <span class="preprocessor"> (values5, 0, (__global DATA_TYPE *)(output_ptr) + (5 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01112"></a><span class="lineno"> 1112</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01113"></a><span class="lineno"> 1113</span> <span class="preprocessor"> (values6, 0, (__global DATA_TYPE *)(output_ptr) + (6 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01114"></a><span class="lineno"> 1114</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01115"></a><span class="lineno"> 1115</span> <span class="preprocessor"> (values7, 0, (__global DATA_TYPE *)(output_ptr) + (7 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01116"></a><span class="lineno"> 1116</span> <span class="preprocessor"> VSTORE(VECTOR_SIZE) \</span></div><div class="line"><a name="l01117"></a><span class="lineno"> 1117</span> <span class="preprocessor"> (values8, 0, (__global DATA_TYPE *)(output_ptr) + (8 + i * 9) * SRC_DEPTH); \</span></div><div class="line"><a name="l01118"></a><span class="lineno"> 1118</span> <span class="preprocessor"> })</span></div><div class="line"><a name="l01119"></a><span class="lineno"> 1119</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_LEFT != 0 || PAD_BOTTOM != 0 || PAD_RIGHT != 0</span></div><div class="line"><a name="l01120"></a><span class="lineno"> 1120</span> <span class="comment"></span></div><div class="line"><a name="l01121"></a><span class="lineno"> 1121</span> <span class="comment">/** This kernel performs im2col when the kernel size is 9x9 and the data layout is NHWC</span></div><div class="line"><a name="l01122"></a><span class="lineno"> 1122</span> <span class="comment"> *</span></div><div class="line"><a name="l01123"></a><span class="lineno"> 1123</span> <span class="comment"> * @note This kernel computes VECTOR_SIZE elements</span></div><div class="line"><a name="l01124"></a><span class="lineno"> 1124</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l01125"></a><span class="lineno"> 1125</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l01126"></a><span class="lineno"> 1126</span> <span class="comment"> * @note The kernel depth must be passed at compile time using -DSRC_DEPTH: e.g. -DSRC_DEPTH=3</span></div><div class="line"><a name="l01127"></a><span class="lineno"> 1127</span> <span class="comment"> * @note The stride along the Y direction must be passed at compile time using -DSTRIDE_Y: e.g. -DSTRIDE_Y=1</span></div><div class="line"><a name="l01128"></a><span class="lineno"> 1128</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</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> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l01131"></a><span class="lineno"> 1131</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="l01132"></a><span class="lineno"> 1132</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="l01133"></a><span class="lineno"> 1133</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="l01134"></a><span class="lineno"> 1134</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="l01135"></a><span class="lineno"> 1135</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="l01136"></a><span class="lineno"> 1136</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l01137"></a><span class="lineno"> 1137</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="l01138"></a><span class="lineno"> 1138</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="l01139"></a><span class="lineno"> 1139</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="l01140"></a><span class="lineno"> 1140</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="l01141"></a><span class="lineno"> 1141</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="l01142"></a><span class="lineno"> 1142</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="l01143"></a><span class="lineno"> 1143</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="l01144"></a><span class="lineno"> 1144</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="l01145"></a><span class="lineno"> 1145</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="l01146"></a><span class="lineno"> 1146</span> <span class="comment"> */</span></div><div class="line"><a name="l01147"></a><span class="lineno"> 1147</span> __kernel <span class="keywordtype">void</span> im2col9x9_nhwc(</div><div class="line"><a name="l01148"></a><span class="lineno"> 1148</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="l01149"></a><span class="lineno"> 1149</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="l01150"></a><span class="lineno"> 1150</span>  uint src_stride_w,</div><div class="line"><a name="l01151"></a><span class="lineno"> 1151</span>  uint dst_stride_w)</div><div class="line"><a name="l01152"></a><span class="lineno"> 1152</span> {</div><div class="line"><a name="l01153"></a><span class="lineno"> 1153</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = min((<span class="keywordtype">int</span>)(get_global_id(0) * <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>), LAST_ACCESSED); <span class="comment">// input feature map</span></div><div class="line"><a name="l01154"></a><span class="lineno"> 1154</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = get_global_id(1);</div><div class="line"><a name="l01155"></a><span class="lineno"> 1155</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2); <span class="comment">// batch size</span></div><div class="line"><a name="l01156"></a><span class="lineno"> 1156</span> </div><div class="line"><a name="l01157"></a><span class="lineno"> 1157</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l01158"></a><span class="lineno"> 1158</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;</div><div class="line"><a name="l01159"></a><span class="lineno"> 1159</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;</div><div class="line"><a name="l01160"></a><span class="lineno"> 1160</span> </div><div class="line"><a name="l01161"></a><span class="lineno"> 1161</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l01162"></a><span class="lineno"> 1162</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) + batch * (<span class="keywordtype">int</span>)src_stride_w;</div><div class="line"><a name="l01163"></a><span class="lineno"> 1163</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) + yo * (<span class="keywordtype">int</span>)dst_stride_y + batch * (int)dst_stride_w;</div><div class="line"><a name="l01164"></a><span class="lineno"> 1164</span> </div><div class="line"><a name="l01165"></a><span class="lineno"> 1165</span>  <span class="keywordtype">int</span> yi_coord = 0;</div><div class="line"><a name="l01166"></a><span class="lineno"> 1166</span>  int8 offset0 = 0;</div><div class="line"><a name="l01167"></a><span class="lineno"> 1167</span>  <span class="keywordtype">int</span> offset1 = 0;</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">// Clamp xi</span></div><div class="line"><a name="l01170"></a><span class="lineno"> 1170</span>  int8 xi_offset0 = ((int8)xi + (int8)(0, 1, 2, 3, 4, 5, 6, 7) * DILATION_X - (int8)PAD_LEFT);</div><div class="line"><a name="l01171"></a><span class="lineno"> 1171</span>  <span class="keywordtype">int</span> xi_offset1 = ((int)xi + (<span class="keywordtype">int</span>)(8) * DILATION_X - (<span class="keywordtype">int</span>)PAD_LEFT);</div><div class="line"><a name="l01172"></a><span class="lineno"> 1172</span> </div><div class="line"><a name="l01173"></a><span class="lineno"> 1173</span> <span class="preprocessor">#if PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l01174"></a><span class="lineno"> 1174</span> <span class="preprocessor">#define CLAMP(x, min_val, max_val) min(max(x, min_val), max_val)</span></div><div class="line"><a name="l01175"></a><span class="lineno"> 1175</span>  xi_offset0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aabdbe431f2713c5c2604cb9872b66aab">CLAMP</a>(xi_offset0, (int8)0, (int8)(SRC_WIDTH - 1));</div><div class="line"><a name="l01176"></a><span class="lineno"> 1176</span>  xi_offset1 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aabdbe431f2713c5c2604cb9872b66aab">CLAMP</a>(xi_offset1, (<span class="keywordtype">int</span>)0, (<span class="keywordtype">int</span>)(SRC_WIDTH - 1));</div><div class="line"><a name="l01177"></a><span class="lineno"> 1177</span> <span class="preprocessor">#endif // PAD_TOP != 0 || PAD_BOTTOM != 0</span></div><div class="line"><a name="l01178"></a><span class="lineno"> 1178</span>  xi_offset0 *= (int8)src_stride_y;</div><div class="line"><a name="l01179"></a><span class="lineno"> 1179</span>  xi_offset1 *= (int)src_stride_y;</div><div class="line"><a name="l01180"></a><span class="lineno"> 1180</span> </div><div class="line"><a name="l01181"></a><span class="lineno"> 1181</span>  <span class="comment">// Out-of-bound condition for X</span></div><div class="line"><a name="l01182"></a><span class="lineno"> 1182</span>  int8 x_cond0 = (((int8)xi + (int8)(0, 1, 2, 3, 4, 5, 6, 7) * DILATION_X - (int8)PAD_LEFT) < (int8)0) || (((int8)xi + (int8)(0, 1, 2, 3, 4, 5, 6, 7) * DILATION_X - (int8)PAD_LEFT) >= (int8)SRC_WIDTH);</div><div class="line"><a name="l01183"></a><span class="lineno"> 1183</span>  <span class="keywordtype">int</span> x_cond1 = (((int)xi + (<span class="keywordtype">int</span>)(8) * DILATION_X - (<span class="keywordtype">int</span>)PAD_LEFT) < (<span class="keywordtype">int</span>)0) || (((<span class="keywordtype">int</span>)xi + (int)(8) * DILATION_X - (int)PAD_LEFT) >= (int)SRC_WIDTH);</div><div class="line"><a name="l01184"></a><span class="lineno"> 1184</span> </div><div class="line"><a name="l01185"></a><span class="lineno"> 1185</span>  IM2COL1x9(0);</div><div class="line"><a name="l01186"></a><span class="lineno"> 1186</span>  IM2COL1x9(1);</div><div class="line"><a name="l01187"></a><span class="lineno"> 1187</span>  IM2COL1x9(2);</div><div class="line"><a name="l01188"></a><span class="lineno"> 1188</span>  IM2COL1x9(3);</div><div class="line"><a name="l01189"></a><span class="lineno"> 1189</span>  IM2COL1x9(4);</div><div class="line"><a name="l01190"></a><span class="lineno"> 1190</span>  IM2COL1x9(5);</div><div class="line"><a name="l01191"></a><span class="lineno"> 1191</span>  IM2COL1x9(6);</div><div class="line"><a name="l01192"></a><span class="lineno"> 1192</span>  IM2COL1x9(7);</div><div class="line"><a name="l01193"></a><span class="lineno"> 1193</span>  IM2COL1x9(8);</div><div class="line"><a name="l01194"></a><span class="lineno"> 1194</span> </div><div class="line"><a name="l01195"></a><span class="lineno"> 1195</span> <span class="preprocessor">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01196"></a><span class="lineno"> 1196</span>  <span class="keywordflow">if</span>((ch + <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>) >= SRC_DEPTH)</div><div class="line"><a name="l01197"></a><span class="lineno"> 1197</span>  {</div><div class="line"><a name="l01198"></a><span class="lineno"> 1198</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) - ch + SRC_DEPTH * 81) = 1.0f;</div><div class="line"><a name="l01199"></a><span class="lineno"> 1199</span>  }</div><div class="line"><a name="l01200"></a><span class="lineno"> 1200</span> <span class="preprocessor">#endif // HAS_BIAS</span></div><div class="line"><a name="l01201"></a><span class="lineno"> 1201</span> }</div><div class="line"><a name="l01202"></a><span class="lineno"> 1202</span> <span class="comment"></span></div><div class="line"><a name="l01203"></a><span class="lineno"> 1203</span> <span class="comment">/** This opencl kernel performs a generic im2col implementation when the data layout is NHWC</span></div><div class="line"><a name="l01204"></a><span class="lineno"> 1204</span> <span class="comment"> *</span></div><div class="line"><a name="l01205"></a><span class="lineno"> 1205</span> <span class="comment"> * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float</span></div><div class="line"><a name="l01206"></a><span class="lineno"> 1206</span> <span class="comment"> * @note The width and height of the input tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT: e.g. -DSRC_WIDTH=128 and -DSRC_HEIGHT=128</span></div><div class="line"><a name="l01207"></a><span class="lineno"> 1207</span> <span class="comment"> * @note The width of output tensor after matrix multiplication must be passed at compile time using -DCONVOLVED_WIDTH: e.g. -DCONVOLVED_WIDTH=34</span></div><div class="line"><a name="l01208"></a><span class="lineno"> 1208</span> <span class="comment"> * @note The kernel width, height and depth must be passed at compile time using -DKERNEL_WIDTH, -DKERNEL_HEIGHT and -DSRC_DEPTH: e.g. -DKERNEL_WIDTH=3, -DKERNEL_HEIGHT=3 and -DSRC_DEPTH=64</span></div><div class="line"><a name="l01209"></a><span class="lineno"> 1209</span> <span class="comment"> * @note The pad_left, pad_right, pad_top and pad_bottom must be passed at compile time using -DPAD_LEFT, -DPAD_RIGHT, -DPAD_TOP and -DPAD_BOTTOM: e.g. -DPAD_LEFT=1, -DPAD_RIGHT=2, -DPAD_TOP=3 and -DPAD_BOTTOM=2</span></div><div class="line"><a name="l01210"></a><span class="lineno"> 1210</span> <span class="comment"> * @note The zero value to store in case we load values out-of-bounds must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0.0</span></div><div class="line"><a name="l01211"></a><span class="lineno"> 1211</span> <span class="comment"> * @note The stride along the X and Y directions must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y: e.g. -DSTRIDE_X=1 and -DSTRIDE_Y=1</span></div><div class="line"><a name="l01212"></a><span class="lineno"> 1212</span> <span class="comment"> * @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1</span></div><div class="line"><a name="l01213"></a><span class="lineno"> 1213</span> <span class="comment"> * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row.</span></div><div class="line"><a name="l01214"></a><span class="lineno"> 1214</span> <span class="comment"> *</span></div><div class="line"><a name="l01215"></a><span class="lineno"> 1215</span> <span class="comment"> * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/F16/F32</span></div><div class="line"><a name="l01216"></a><span class="lineno"> 1216</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="l01217"></a><span class="lineno"> 1217</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="l01218"></a><span class="lineno"> 1218</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="l01219"></a><span class="lineno"> 1219</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="l01220"></a><span class="lineno"> 1220</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="l01221"></a><span class="lineno"> 1221</span> <span class="comment"> * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes)</span></div><div class="line"><a name="l01222"></a><span class="lineno"> 1222</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="l01223"></a><span class="lineno"> 1223</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="l01224"></a><span class="lineno"> 1224</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="l01225"></a><span class="lineno"> 1225</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="l01226"></a><span class="lineno"> 1226</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="l01227"></a><span class="lineno"> 1227</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="l01228"></a><span class="lineno"> 1228</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="l01229"></a><span class="lineno"> 1229</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="l01230"></a><span class="lineno"> 1230</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="l01231"></a><span class="lineno"> 1231</span> <span class="comment"> */</span></div><div class="line"><a name="l01232"></a><span class="lineno"> 1232</span> __kernel <span class="keywordtype">void</span> im2col_generic_nhwc(</div><div class="line"><a name="l01233"></a><span class="lineno"> 1233</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="l01234"></a><span class="lineno"> 1234</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="l01235"></a><span class="lineno"> 1235</span>  uint src_stride_w,</div><div class="line"><a name="l01236"></a><span class="lineno"> 1236</span>  uint dst_stride_w)</div><div class="line"><a name="l01237"></a><span class="lineno"> 1237</span> {</div><div class="line"><a name="l01238"></a><span class="lineno"> 1238</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> ch = min((<span class="keywordtype">int</span>)(get_global_id(0) * <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>), LAST_ACCESSED); <span class="comment">// input feature map</span></div><div class="line"><a name="l01239"></a><span class="lineno"> 1239</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yo = get_global_id(1);</div><div class="line"><a name="l01240"></a><span class="lineno"> 1240</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> batch = get_global_id(2); <span class="comment">// batch size</span></div><div class="line"><a name="l01241"></a><span class="lineno"> 1241</span> </div><div class="line"><a name="l01242"></a><span class="lineno"> 1242</span>  <span class="comment">// Calculate input indices</span></div><div class="line"><a name="l01243"></a><span class="lineno"> 1243</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> xi = (get_global_id(1) % CONVOLVED_WIDTH) * STRIDE_X;</div><div class="line"><a name="l01244"></a><span class="lineno"> 1244</span>  <span class="keyword">const</span> <span class="keywordtype">int</span> yi = (get_global_id(1) / (int)CONVOLVED_WIDTH) * STRIDE_Y;</div><div class="line"><a name="l01245"></a><span class="lineno"> 1245</span> </div><div class="line"><a name="l01246"></a><span class="lineno"> 1246</span>  <span class="comment">// Get input and output address</span></div><div class="line"><a name="l01247"></a><span class="lineno"> 1247</span>  __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + ch * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) + batch * (<span class="keywordtype">int</span>)src_stride_w;</div><div class="line"><a name="l01248"></a><span class="lineno"> 1248</span>  __global uchar *output_ptr = dst_ptr + dst_offset_first_element_in_bytes + ch * <span class="keyword">sizeof</span>(<a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a>) + yo * (<span class="keywordtype">int</span>)dst_stride_y + batch * (int)dst_stride_w;</div><div class="line"><a name="l01249"></a><span class="lineno"> 1249</span> </div><div class="line"><a name="l01250"></a><span class="lineno"> 1250</span>  <span class="keywordtype">int</span> i = 0;</div><div class="line"><a name="l01251"></a><span class="lineno"> 1251</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> yk = 0; yk < KERNEL_HEIGHT; ++yk)</div><div class="line"><a name="l01252"></a><span class="lineno"> 1252</span>  {</div><div class="line"><a name="l01253"></a><span class="lineno"> 1253</span>  <span class="comment">// Clamp yi_coord</span></div><div class="line"><a name="l01254"></a><span class="lineno"> 1254</span>  <span class="keywordtype">int</span> yi_coord = yi + yk * DILATION_Y - (int)PAD_TOP;</div><div class="line"><a name="l01255"></a><span class="lineno"> 1255</span>  yi_coord = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aabdbe431f2713c5c2604cb9872b66aab">CLAMP</a>(yi_coord, (<span class="keywordtype">int</span>)0, (<span class="keywordtype">int</span>)(SRC_HEIGHT - 1));</div><div class="line"><a name="l01256"></a><span class="lineno"> 1256</span> </div><div class="line"><a name="l01257"></a><span class="lineno"> 1257</span>  <span class="comment">// Out-of-bound condition for Y</span></div><div class="line"><a name="l01258"></a><span class="lineno"> 1258</span>  <span class="keywordtype">int</span> y_border_condition = ((yi + yk * DILATION_Y - (int)PAD_TOP) < (int)0) || ((yi + yk * DILATION_Y - (int)PAD_TOP) >= (int)SRC_HEIGHT);</div><div class="line"><a name="l01259"></a><span class="lineno"> 1259</span> </div><div class="line"><a name="l01260"></a><span class="lineno"> 1260</span>  <span class="keywordflow">for</span>(<span class="keywordtype">int</span> xk = 0; xk < KERNEL_WIDTH; ++xk)</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>  <span class="comment">// Clamp xi_coord</span></div><div class="line"><a name="l01263"></a><span class="lineno"> 1263</span>  <span class="keywordtype">int</span> xi_coord = (xi + xk * DILATION_X - (int)PAD_LEFT);</div><div class="line"><a name="l01264"></a><span class="lineno"> 1264</span>  xi_coord = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#aabdbe431f2713c5c2604cb9872b66aab">CLAMP</a>(xi_coord, (<span class="keywordtype">int</span>)0, (<span class="keywordtype">int</span>)(SRC_WIDTH - 1));</div><div class="line"><a name="l01265"></a><span class="lineno"> 1265</span> </div><div class="line"><a name="l01266"></a><span class="lineno"> 1266</span>  <span class="comment">// Out-of-bound condition for X</span></div><div class="line"><a name="l01267"></a><span class="lineno"> 1267</span>  <span class="keywordtype">int</span> x_border_condition = ((xi + xk * DILATION_X - (int)PAD_LEFT) < (int)0) || ((xi + xk * DILATION_X - (int)PAD_LEFT) >= (int)SRC_WIDTH);</div><div class="line"><a name="l01268"></a><span class="lineno"> 1268</span> </div><div class="line"><a name="l01269"></a><span class="lineno"> 1269</span>  <span class="keywordtype">int</span> <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a> = xi_coord * (int)src_stride_y + (yi_coord * (<span class="keywordtype">int</span>)src_stride_z);</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>  VECTOR_N values0 = <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a287e2fc366c312b468382c95bb90f91f">VLOAD</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)(0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(input_ptr + <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a009469e4d9b8fce3b6d5e97d2077827d">offset</a>));</div><div class="line"><a name="l01272"></a><span class="lineno"> 1272</span> </div><div class="line"><a name="l01273"></a><span class="lineno"> 1273</span>  <span class="comment">// Replace with PAD_VALUE if the value is out-of-bound</span></div><div class="line"><a name="l01274"></a><span class="lineno"> 1274</span>  values0 = <a class="code" href="namespacearm__compute_1_1test_1_1validation.xhtml#af77145fbdc6b0c8931148f5597d9de53">select</a>(values0, (VECTOR_N)PAD_VALUE, (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))x_border_condition || (<a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#a36f754c05b6fddf6df0d8d0a74f8159f">VEC_DATA_TYPE</a>(COND_DATA_TYPE, <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>))(y_border_condition));</div><div class="line"><a name="l01275"></a><span class="lineno"> 1275</span> </div><div class="line"><a name="l01276"></a><span class="lineno"> 1276</span>  <span class="comment">// Store</span></div><div class="line"><a name="l01277"></a><span class="lineno"> 1277</span>  <a class="code" href="src_2core_2_c_l_2cl__kernels_2_helpers_8h.xhtml#acb282042d1edeeaa3cc979a206f78b54">VSTORE</a>(<a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>)</div><div class="line"><a name="l01278"></a><span class="lineno"> 1278</span>  (values0, 0, (__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) + i * (int)SRC_DEPTH);</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>  i++;</div><div class="line"><a name="l01281"></a><span class="lineno"> 1281</span>  }</div><div class="line"><a name="l01282"></a><span class="lineno"> 1282</span>  }</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">#ifdef HAS_BIAS</span></div><div class="line"><a name="l01285"></a><span class="lineno"> 1285</span>  <span class="keywordflow">if</span>((ch + <a class="code" href="softmax__layer_8cl.xhtml#a7c78836761fa3b5b124efea237dac70f">VECTOR_SIZE</a>) >= SRC_DEPTH)</div><div class="line"><a name="l01286"></a><span class="lineno"> 1286</span>  {</div><div class="line"><a name="l01287"></a><span class="lineno"> 1287</span>  *((__global <a class="code" href="convolution3x3_8cl.xhtml#afb8c72ce35c4a1f4a2588d6573e54aa1">DATA_TYPE</a> *)(output_ptr) - ch + SRC_DEPTH * KERNEL_WIDTH * KERNEL_HEIGHT) = 1.0f;</div><div class="line"><a name="l01288"></a><span class="lineno"> 1288</span>  }</div><div class="line"><a name="l01289"></a><span class="lineno"> 1289</span> <span class="preprocessor">#endif // HAS_BIAS</span></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="preprocessor">#endif // defined(CONVOLVED_WIDTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_DEPTH) && defined(PAD_LEFT) && defined(PAD_RIGHT) && defined(PAD_TOP) && defined(PAD_BOTTOM) && defined(PAD_VALUE) && defined(VECTOR_SIZE) && defined(LAST_ACCESSED)</span></div><div class="line"><a name="l01292"></a><span class="lineno"> 1292</span> <span class="preprocessor">#endif // defined(DATA_TYPE) && defined(ELEMENT_SIZE)</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> |