Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
Tetragramm
GitHub Repository: Tetragramm/opencv
Path: blob/master/modules/dnn/src/opencl/ocl4dnn_lrn.cl
16337 views
1
/*M///////////////////////////////////////////////////////////////////////////////////////
2
//
3
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
4
//
5
// By downloading, copying, installing or using the software you agree to this license.
6
// If you do not agree to this license, do not download, install,
7
// copy or use the software.
8
//
9
//
10
// License Agreement
11
// For Open Source Computer Vision Library
12
//
13
// Copyright (C) 2017, Intel Corporation, all rights reserved.
14
// Copyright (c) 2016-2017 Fabian David Tschopp, all rights reserved.
15
// Third party copyrights are property of their respective owners.
16
//
17
// Redistribution and use in source and binary forms, with or without modification,
18
// are permitted provided that the following conditions are met:
19
//
20
// * Redistribution's of source code must retain the above copyright notice,
21
// this list of conditions and the following disclaimer.
22
//
23
// * Redistribution's in binary form must reproduce the above copyright notice,
24
// this list of conditions and the following disclaimer in the documentation
25
// and/or other materials provided with the distribution.
26
//
27
// * The name of the copyright holders may not be used to endorse or promote products
28
// derived from this software without specific prior written permission.
29
//
30
// This software is provided by the copyright holders and contributors "as is" and
31
// any express or implied warranties, including, but not limited to, the implied
32
// warranties of merchantability and fitness for a particular purpose are disclaimed.
33
// In no event shall the Intel Corporation or contributors be liable for any direct,
34
// indirect, incidental, special, exemplary, or consequential damages
35
// (including, but not limited to, procurement of substitute goods or services;
36
// loss of use, data, or profits; or business interruption) however caused
37
// and on any theory of liability, whether in contract, strict liability,
38
// or tort (including negligence or otherwise) arising in any way out of
39
// the use of this software, even if advised of the possibility of such damage.
40
//
41
//M*/
42
43
#define CONCAT(A,B) A##_##B
44
#define TEMPLATE(name,type) CONCAT(name,type)
45
#define KERNEL_ARG_DTYPE float
46
47
#if defined(cl_khr_fp16)
48
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
49
#endif
50
51
__kernel void TEMPLATE(lrn_full_no_scale,Dtype)(const int nthreads, __global const Dtype* in,
52
const int num, const int channels,
53
const int height, const int width, const int size,
54
const KERNEL_ARG_DTYPE alpha_over_size, const KERNEL_ARG_DTYPE k,
55
__global Dtype* const out,
56
const KERNEL_ARG_DTYPE negative_beta) {
57
for (int index = get_global_id(0); index < nthreads;
58
index += get_global_size(0)) {
59
// find out the local offset
60
const int w = index % width;
61
const int h = (index / width) % height;
62
const int n = index / width / height;
63
const int offset = (n * channels * height + h) * width + w;
64
const int step = height * width;
65
__global const Dtype* in_off = in + offset;
66
__global Dtype* out_off = out + offset;
67
KERNEL_ARG_DTYPE scale_val;
68
int head = 0;
69
const int pre_pad = (size - 1) / 2;
70
const int post_pad = size - pre_pad - 1;
71
KERNEL_ARG_DTYPE accum_scale = 0;
72
// fill the scale at [n, :, h, w]
73
// accumulate values
74
while (head < post_pad && head < channels) {
75
accum_scale += in_off[head * step] * in_off[head * step];
76
++head;
77
}
78
// both add and subtract
79
while (head < channels) {
80
accum_scale += in_off[head * step] * in_off[head * step];
81
if (head - size >= 0) {
82
accum_scale -= in_off[(head - size) * step]
83
* in_off[(head - size) * step];
84
}
85
scale_val = k + accum_scale * alpha_over_size;
86
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
87
++head;
88
}
89
// subtract only
90
while (head < channels + post_pad) {
91
if (head - size >= 0) {
92
accum_scale -= in_off[(head - size) * step]
93
* in_off[(head - size) * step];
94
}
95
scale_val = k + accum_scale * alpha_over_size;
96
out_off[(head - post_pad) * step] = in_off[(head - post_pad) * step] * (Dtype)native_powr((Dtype)scale_val, (Dtype)negative_beta);
97
++head;
98
}
99
}
100
}
101
102