1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2017, Intel Corporation, all rights reserved.
// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#define CONCAT(A,B) A##_##B
#define TEMPLATE(name,type) CONCAT(name,type)
#define KERNEL_ARG_DTYPE float
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global const Dtype* in,
const int num, const int channels,
const int height, const int width, const int size,
const KERNEL_ARG_DTYPE alpha_over_size, const KERNEL_ARG_DTYPE k,
__global Dtype* const out,
const KERNEL_ARG_DTYPE negative_beta) {
for (int index = get_global_id(0); index < nthreads;
index += get_global_size(0)) {
// find out the local offset
const int w = index % width;
const int h = (index / width) % height;
const int n = index / width / height;
const int offset = (n * channels * height + h) * width + w;
const int step = height * width;
__global const Dtype* in_off = in + offset;
__global Dtype* out_off = out + offset;
KERNEL_ARG_DTYPE scale_val;
int head = 0;
const int pre_pad = (size - 1) / 2;
const int post_pad = size - pre_pad - 1;
KERNEL_ARG_DTYPE accum_scale = 0;
// fill the scale at [n, :, h, w]
// accumulate values
while (head < post_pad && head < channels) {
accum_scale += in_off[head * step] * in_off[head * step];
++head;
}
// both add and subtract
while (head < channels) {
accum_scale += in_off[head * step] * in_off[head * step];
if (head - size >= 0) {
accum_scale -= in_off[(head - size) * step]
* in_off[(head - size) * step];
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
++head;
}
// subtract only
while (head < channels + post_pad) {
if (head - size >= 0) {
accum_scale -= in_off[(head - size) * step]
* in_off[(head - size) * step];
}
scale_val = k + accum_scale * alpha_over_size;
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
++head;
}
}
}