forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
roi_align_op.cu
194 lines (165 loc) · 5.42 KB
/
roi_align_op.cu
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
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
#include "caffe2/operators/roi_align_op.h"
#include <stdio.h>
#include <cfloat>
#include "caffe2/core/context_gpu.h"
#include "caffe2/utils/math.h"
namespace caffe2 {
namespace {
template <typename T>
__device__ T bilinear_interpolate(
const T* bottom_data,
const int height,
const int width,
T y,
T x,
const int index /* index for debug only*/) {
// deal with cases that inverse elements are out of feature map boundary
if (y < -1.0 || y > height || x < -1.0 || x > width) {
// empty
return 0;
}
if (y <= 0) {
y = 0;
}
if (x <= 0) {
x = 0;
}
int y_low = (int)y;
int x_low = (int)x;
int y_high;
int x_high;
if (y_low >= height - 1) {
y_high = y_low = height - 1;
y = (T)y_low;
} else {
y_high = y_low + 1;
}
if (x_low >= width - 1) {
x_high = x_low = width - 1;
x = (T)x_low;
} else {
x_high = x_low + 1;
}
T ly = y - y_low;
T lx = x - x_low;
T hy = 1. - ly, hx = 1. - lx;
// do bilinear interpolation
T v1 = bottom_data[y_low * width + x_low];
T v2 = bottom_data[y_low * width + x_high];
T v3 = bottom_data[y_high * width + x_low];
T v4 = bottom_data[y_high * width + x_high];
T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;
T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
return val;
}
template <typename T>
__global__ void RoIAlignForward(
const int nthreads,
const T* bottom_data,
const T spatial_scale,
const int channels,
const int height,
const int width,
const int pooled_height,
const int pooled_width,
const int sampling_ratio,
const T* bottom_rois,
int roi_cols,
T* top_data,
bool continuous_coordinate) {
CUDA_1D_KERNEL_LOOP(index, nthreads) {
// (n, c, ph, pw) is an element in the pooled output
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
// RoI could have 4 or 5 columns
const T* offset_bottom_rois = bottom_rois + n * roi_cols;
int roi_batch_ind = 0;
if (roi_cols == 5) {
roi_batch_ind = offset_bottom_rois[0];
offset_bottom_rois++;
}
// Do not using rounding; this implementation detail is critical
T roi_offset = continuous_coordinate ? T(0.5) : 0;
T roi_start_w = offset_bottom_rois[0] * spatial_scale - roi_offset;
T roi_start_h = offset_bottom_rois[1] * spatial_scale - roi_offset;
T roi_end_w = offset_bottom_rois[2] * spatial_scale - roi_offset;
T roi_end_h = offset_bottom_rois[3] * spatial_scale - roi_offset;
T roi_width = roi_end_w - roi_start_w;
T roi_height = roi_end_h - roi_start_h;
if (!continuous_coordinate) { // backward compatibility
// Force malformed ROIs to be 1x1
roi_width = c10::cuda::compat::max(roi_width, (T)1.);
roi_height = c10::cuda::compat::max(roi_height, (T)1.);
}
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
const T* offset_bottom_data =
bottom_data + (roi_batch_ind * channels + c) * height * width;
// We use roi_bin_grid to sample the grid and mimic integral
int roi_bin_grid_h = (sampling_ratio > 0)
? sampling_ratio
: ceil(roi_height / pooled_height); // e.g., = 2
int roi_bin_grid_w =
(sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);
// We do average (integral) pooling inside a bin
const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4
T output_val = 0.;
for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1
{
const T y = roi_start_h + ph * bin_size_h +
static_cast<T>(iy + .5f) * bin_size_h /
static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5
for (int ix = 0; ix < roi_bin_grid_w; ix++) {
const T x = roi_start_w + pw * bin_size_w +
static_cast<T>(ix + .5f) * bin_size_w /
static_cast<T>(roi_bin_grid_w);
T val = bilinear_interpolate(
offset_bottom_data, height, width, y, x, index);
output_val += val;
}
}
output_val /= count;
top_data[index] = output_val;
}
}
} // namespace
template <>
bool RoIAlignOp<float, CUDAContext>::RunOnDevice() {
auto& X = Input(0); // Input data to pool
auto& R = Input(1); // RoIs
// RoI pooled data
if (R.numel() == 0) {
// Handle empty rois
Output(0, {0, X.dim32(1), pooled_h_, pooled_w_}, at::dtype<float>());
return true;
}
assert(sampling_ratio_ >= 0);
auto* Y = Output(
0, {R.dim32(0), X.dim32(1), pooled_h_, pooled_w_}, at::dtype<float>());
int output_size = Y->numel();
RoIAlignForward<float>
<<<CAFFE_GET_BLOCKS(output_size),
CAFFE_CUDA_NUM_THREADS,
0,
context_.cuda_stream()>>>(
output_size,
X.data<float>(),
spatial_scale_,
X.dim32(1),
X.dim32(2),
X.dim32(3),
pooled_h_,
pooled_w_,
sampling_ratio_,
R.data<float>(),
R.dim32(1),
Y->mutable_data<float>(),
aligned_);
return true;
}
REGISTER_CUDA_OPERATOR(RoIAlign, RoIAlignOp<float, CUDAContext>);
} // namespace caffe2
using RoIAlignOpFloatCUDA = caffe2::RoIAlignOp<float, caffe2::CUDAContext>;
C10_EXPORT_CAFFE2_OP_TO_C10_CUDA(RoIAlign, RoIAlignOpFloatCUDA);