forked from AlexeyAB/darknet
-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathcol2im.c
95 lines (91 loc) · 3.52 KB
/
col2im.c
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
#include <stdio.h>
#include <math.h>
#include <string.h>
#include "col2im.h"
void col2im_add_pixel(float *im, int height, int width, int channels,
int row, int col, int channel, int pad, float val)
{
row -= pad;
col -= pad;
if (row < 0 || col < 0 ||
row >= height || col >= width) return;
im[col + width*(row + height*channel)] += val;
}
//This one might be too, can't remember.
void col2im_cpu(float* data_col,
int channels, int height, int width,
int ksize, int stride, int pad, float* data_im)
{
int c,h,w;
int height_col = (height + 2*pad - ksize) / stride + 1;
int width_col = (width + 2*pad - ksize) / stride + 1;
int channels_col = channels * ksize * ksize;
for (c = 0; c < channels_col; ++c) {
int w_offset = c % ksize;
int h_offset = (c / ksize) % ksize;
int c_im = c / ksize / ksize;
for (h = 0; h < height_col; ++h) {
for (w = 0; w < width_col; ++w) {
int im_row = h_offset + h * stride;
int im_col = w_offset + w * stride;
int col_index = (c * height_col + h) * width_col + w;
float val = data_col[col_index];
col2im_add_pixel(data_im, height, width, channels,
im_row, im_col, c_im, pad, val);
}
}
}
}
// ----------------------------------------
void caffe_set(const int N, const float alpha, float* Y) {
if (alpha == 0) {
memset(Y, 0, sizeof(float) * N); // NOLINT(caffe/alt_fn)
return;
}
int i;
for (i = 0; i < N; ++i) {
Y[i] = alpha;
}
}
inline static int is_a_ge_zero_and_a_lt_b(int a, int b) {
return (unsigned)(a) < (unsigned)(b);
}
// https://github.com/BVLC/caffe/blob/master/src/caffe/util/im2col.cpp
void col2im_cpu_ext(const float* data_col, const int channels,
const int height, const int width, const int kernel_h, const int kernel_w,
const int pad_h, const int pad_w,
const int stride_h, const int stride_w,
const int dilation_h, const int dilation_w,
float* data_im)
{
caffe_set(height * width * channels, 0.0F, data_im);
const int output_h = (height + 2 * pad_h -
(dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
const int output_w = (width + 2 * pad_w -
(dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
const int channel_size = height * width;
int channel, kernel_row, kernel_col, output_rows, output_col;
for (channel = channels; channel--; data_im += channel_size) {
for (kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
for (kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
int input_row = -pad_h + kernel_row * dilation_h;
for (output_rows = output_h; output_rows; output_rows--) {
if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
data_col += output_w;
}
else {
int input_col = -pad_w + kernel_col * dilation_w;
for (output_col = output_w; output_col; output_col--) {
if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
data_im[input_row * width + input_col] += *data_col;
}
data_col++;
input_col += stride_w;
}
}
input_row += stride_h;
}
}
}
}
}