File size: 13,153 Bytes
34d1f8b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 |
# Copyright (c) OpenMMLab. All rights reserved.
#####################
# Based on https://github.com/hongzhenwang/RRPN-revise
# Licensed under The MIT License
# Author: yanyan, scrin@foxmail.com
#####################
import math
import numba
import numpy as np
from numba import cuda
@numba.jit(nopython=True)
def div_up(m, n):
return m // n + (m % n > 0)
@cuda.jit(device=True, inline=True)
def trangle_area(a, b, c):
return ((a[0] - c[0]) * (b[1] - c[1]) - (a[1] - c[1]) *
(b[0] - c[0])) / 2.0
@cuda.jit(device=True, inline=True)
def area(int_pts, num_of_inter):
area_val = 0.0
for i in range(num_of_inter - 2):
area_val += abs(
trangle_area(int_pts[:2], int_pts[2 * i + 2:2 * i + 4],
int_pts[2 * i + 4:2 * i + 6]))
return area_val
@cuda.jit(device=True, inline=True)
def sort_vertex_in_convex_polygon(int_pts, num_of_inter):
if num_of_inter > 0:
center = cuda.local.array((2, ), dtype=numba.float32)
center[:] = 0.0
for i in range(num_of_inter):
center[0] += int_pts[2 * i]
center[1] += int_pts[2 * i + 1]
center[0] /= num_of_inter
center[1] /= num_of_inter
v = cuda.local.array((2, ), dtype=numba.float32)
vs = cuda.local.array((16, ), dtype=numba.float32)
for i in range(num_of_inter):
v[0] = int_pts[2 * i] - center[0]
v[1] = int_pts[2 * i + 1] - center[1]
d = math.sqrt(v[0] * v[0] + v[1] * v[1])
v[0] = v[0] / d
v[1] = v[1] / d
if v[1] < 0:
v[0] = -2 - v[0]
vs[i] = v[0]
j = 0
temp = 0
for i in range(1, num_of_inter):
if vs[i - 1] > vs[i]:
temp = vs[i]
tx = int_pts[2 * i]
ty = int_pts[2 * i + 1]
j = i
while j > 0 and vs[j - 1] > temp:
vs[j] = vs[j - 1]
int_pts[j * 2] = int_pts[j * 2 - 2]
int_pts[j * 2 + 1] = int_pts[j * 2 - 1]
j -= 1
vs[j] = temp
int_pts[j * 2] = tx
int_pts[j * 2 + 1] = ty
@cuda.jit(device=True, inline=True)
def line_segment_intersection(pts1, pts2, i, j, temp_pts):
A = cuda.local.array((2, ), dtype=numba.float32)
B = cuda.local.array((2, ), dtype=numba.float32)
C = cuda.local.array((2, ), dtype=numba.float32)
D = cuda.local.array((2, ), dtype=numba.float32)
A[0] = pts1[2 * i]
A[1] = pts1[2 * i + 1]
B[0] = pts1[2 * ((i + 1) % 4)]
B[1] = pts1[2 * ((i + 1) % 4) + 1]
C[0] = pts2[2 * j]
C[1] = pts2[2 * j + 1]
D[0] = pts2[2 * ((j + 1) % 4)]
D[1] = pts2[2 * ((j + 1) % 4) + 1]
BA0 = B[0] - A[0]
BA1 = B[1] - A[1]
DA0 = D[0] - A[0]
CA0 = C[0] - A[0]
DA1 = D[1] - A[1]
CA1 = C[1] - A[1]
acd = DA1 * CA0 > CA1 * DA0
bcd = (D[1] - B[1]) * (C[0] - B[0]) > (C[1] - B[1]) * (D[0] - B[0])
if acd != bcd:
abc = CA1 * BA0 > BA1 * CA0
abd = DA1 * BA0 > BA1 * DA0
if abc != abd:
DC0 = D[0] - C[0]
DC1 = D[1] - C[1]
ABBA = A[0] * B[1] - B[0] * A[1]
CDDC = C[0] * D[1] - D[0] * C[1]
DH = BA1 * DC0 - BA0 * DC1
Dx = ABBA * DC0 - BA0 * CDDC
Dy = ABBA * DC1 - BA1 * CDDC
temp_pts[0] = Dx / DH
temp_pts[1] = Dy / DH
return True
return False
@cuda.jit(device=True, inline=True)
def line_segment_intersection_v1(pts1, pts2, i, j, temp_pts):
a = cuda.local.array((2, ), dtype=numba.float32)
b = cuda.local.array((2, ), dtype=numba.float32)
c = cuda.local.array((2, ), dtype=numba.float32)
d = cuda.local.array((2, ), dtype=numba.float32)
a[0] = pts1[2 * i]
a[1] = pts1[2 * i + 1]
b[0] = pts1[2 * ((i + 1) % 4)]
b[1] = pts1[2 * ((i + 1) % 4) + 1]
c[0] = pts2[2 * j]
c[1] = pts2[2 * j + 1]
d[0] = pts2[2 * ((j + 1) % 4)]
d[1] = pts2[2 * ((j + 1) % 4) + 1]
area_abc = trangle_area(a, b, c)
area_abd = trangle_area(a, b, d)
if area_abc * area_abd >= 0:
return False
area_cda = trangle_area(c, d, a)
area_cdb = area_cda + area_abc - area_abd
if area_cda * area_cdb >= 0:
return False
t = area_cda / (area_abd - area_abc)
dx = t * (b[0] - a[0])
dy = t * (b[1] - a[1])
temp_pts[0] = a[0] + dx
temp_pts[1] = a[1] + dy
return True
@cuda.jit(device=True, inline=True)
def point_in_quadrilateral(pt_x, pt_y, corners):
ab0 = corners[2] - corners[0]
ab1 = corners[3] - corners[1]
ad0 = corners[6] - corners[0]
ad1 = corners[7] - corners[1]
ap0 = pt_x - corners[0]
ap1 = pt_y - corners[1]
abab = ab0 * ab0 + ab1 * ab1
abap = ab0 * ap0 + ab1 * ap1
adad = ad0 * ad0 + ad1 * ad1
adap = ad0 * ap0 + ad1 * ap1
return abab >= abap and abap >= 0 and adad >= adap and adap >= 0
@cuda.jit(device=True, inline=True)
def quadrilateral_intersection(pts1, pts2, int_pts):
num_of_inter = 0
for i in range(4):
if point_in_quadrilateral(pts1[2 * i], pts1[2 * i + 1], pts2):
int_pts[num_of_inter * 2] = pts1[2 * i]
int_pts[num_of_inter * 2 + 1] = pts1[2 * i + 1]
num_of_inter += 1
if point_in_quadrilateral(pts2[2 * i], pts2[2 * i + 1], pts1):
int_pts[num_of_inter * 2] = pts2[2 * i]
int_pts[num_of_inter * 2 + 1] = pts2[2 * i + 1]
num_of_inter += 1
temp_pts = cuda.local.array((2, ), dtype=numba.float32)
for i in range(4):
for j in range(4):
has_pts = line_segment_intersection(pts1, pts2, i, j, temp_pts)
if has_pts:
int_pts[num_of_inter * 2] = temp_pts[0]
int_pts[num_of_inter * 2 + 1] = temp_pts[1]
num_of_inter += 1
return num_of_inter
@cuda.jit(device=True, inline=True)
def rbbox_to_corners(corners, rbbox):
# generate clockwise corners and rotate it clockwise
angle = rbbox[4]
a_cos = math.cos(angle)
a_sin = math.sin(angle)
center_x = rbbox[0]
center_y = rbbox[1]
x_d = rbbox[2]
y_d = rbbox[3]
corners_x = cuda.local.array((4, ), dtype=numba.float32)
corners_y = cuda.local.array((4, ), dtype=numba.float32)
corners_x[0] = -x_d / 2
corners_x[1] = -x_d / 2
corners_x[2] = x_d / 2
corners_x[3] = x_d / 2
corners_y[0] = -y_d / 2
corners_y[1] = y_d / 2
corners_y[2] = y_d / 2
corners_y[3] = -y_d / 2
for i in range(4):
corners[2 * i] = a_cos * corners_x[i] + a_sin * corners_y[i] + center_x
corners[2 * i +
1] = -a_sin * corners_x[i] + a_cos * corners_y[i] + center_y
@cuda.jit(device=True, inline=True)
def inter(rbbox1, rbbox2):
"""Compute intersection of two rotated boxes.
Args:
rbox1 (np.ndarray, shape=[5]): Rotated 2d box.
rbox2 (np.ndarray, shape=[5]): Rotated 2d box.
Returns:
float: Intersection of two rotated boxes.
"""
corners1 = cuda.local.array((8, ), dtype=numba.float32)
corners2 = cuda.local.array((8, ), dtype=numba.float32)
intersection_corners = cuda.local.array((16, ), dtype=numba.float32)
rbbox_to_corners(corners1, rbbox1)
rbbox_to_corners(corners2, rbbox2)
num_intersection = quadrilateral_intersection(corners1, corners2,
intersection_corners)
sort_vertex_in_convex_polygon(intersection_corners, num_intersection)
# print(intersection_corners.reshape([-1, 2])[:num_intersection])
return area(intersection_corners, num_intersection)
@cuda.jit(device=True, inline=True)
def devRotateIoUEval(rbox1, rbox2, criterion=-1):
"""Compute rotated iou on device.
Args:
rbox1 (np.ndarray, shape=[5]): Rotated 2d box.
rbox2 (np.ndarray, shape=[5]): Rotated 2d box.
criterion (int, optional): Indicate different type of iou.
-1 indicate `area_inter / (area1 + area2 - area_inter)`,
0 indicate `area_inter / area1`,
1 indicate `area_inter / area2`.
Returns:
float: iou between two input boxes.
"""
area1 = rbox1[2] * rbox1[3]
area2 = rbox2[2] * rbox2[3]
area_inter = inter(rbox1, rbox2)
if criterion == -1:
return area_inter / (area1 + area2 - area_inter)
elif criterion == 0:
return area_inter / area1
elif criterion == 1:
return area_inter / area2
else:
return area_inter
@cuda.jit(
'(int64, int64, float32[:], float32[:], float32[:], int32)',
fastmath=False)
def rotate_iou_kernel_eval(N,
K,
dev_boxes,
dev_query_boxes,
dev_iou,
criterion=-1):
"""Kernel of computing rotated IoU. This function is for bev boxes in
camera coordinate system ONLY (the rotation is clockwise).
Args:
N (int): The number of boxes.
K (int): The number of query boxes.
dev_boxes (np.ndarray): Boxes on device.
dev_query_boxes (np.ndarray): Query boxes on device.
dev_iou (np.ndarray): Computed iou to return.
criterion (int, optional): Indicate different type of iou.
-1 indicate `area_inter / (area1 + area2 - area_inter)`,
0 indicate `area_inter / area1`,
1 indicate `area_inter / area2`.
"""
threadsPerBlock = 8 * 8
row_start = cuda.blockIdx.x
col_start = cuda.blockIdx.y
tx = cuda.threadIdx.x
row_size = min(N - row_start * threadsPerBlock, threadsPerBlock)
col_size = min(K - col_start * threadsPerBlock, threadsPerBlock)
block_boxes = cuda.shared.array(shape=(64 * 5, ), dtype=numba.float32)
block_qboxes = cuda.shared.array(shape=(64 * 5, ), dtype=numba.float32)
dev_query_box_idx = threadsPerBlock * col_start + tx
dev_box_idx = threadsPerBlock * row_start + tx
if (tx < col_size):
block_qboxes[tx * 5 + 0] = dev_query_boxes[dev_query_box_idx * 5 + 0]
block_qboxes[tx * 5 + 1] = dev_query_boxes[dev_query_box_idx * 5 + 1]
block_qboxes[tx * 5 + 2] = dev_query_boxes[dev_query_box_idx * 5 + 2]
block_qboxes[tx * 5 + 3] = dev_query_boxes[dev_query_box_idx * 5 + 3]
block_qboxes[tx * 5 + 4] = dev_query_boxes[dev_query_box_idx * 5 + 4]
if (tx < row_size):
block_boxes[tx * 5 + 0] = dev_boxes[dev_box_idx * 5 + 0]
block_boxes[tx * 5 + 1] = dev_boxes[dev_box_idx * 5 + 1]
block_boxes[tx * 5 + 2] = dev_boxes[dev_box_idx * 5 + 2]
block_boxes[tx * 5 + 3] = dev_boxes[dev_box_idx * 5 + 3]
block_boxes[tx * 5 + 4] = dev_boxes[dev_box_idx * 5 + 4]
cuda.syncthreads()
if tx < row_size:
for i in range(col_size):
offset = (
row_start * threadsPerBlock * K + col_start * threadsPerBlock +
tx * K + i)
dev_iou[offset] = devRotateIoUEval(block_qboxes[i * 5:i * 5 + 5],
block_boxes[tx * 5:tx * 5 + 5],
criterion)
def rotate_iou_gpu_eval(boxes, query_boxes, criterion=-1, device_id=0):
"""Rotated box iou running in gpu. 500x faster than cpu version (take 5ms
in one example with numba.cuda code). convert from [this project](
https://github.com/hongzhenwang/RRPN-revise/tree/master/lib/rotation).
This function is for bev boxes in camera coordinate system ONLY
(the rotation is clockwise).
Args:
boxes (torch.Tensor): rbboxes. format: centers, dims,
angles(clockwise when positive) with the shape of [N, 5].
query_boxes (torch.FloatTensor, shape=(K, 5)):
rbboxes to compute iou with boxes.
device_id (int, optional): Defaults to 0. Device to use.
criterion (int, optional): Indicate different type of iou.
-1 indicate `area_inter / (area1 + area2 - area_inter)`,
0 indicate `area_inter / area1`,
1 indicate `area_inter / area2`.
Returns:
np.ndarray: IoU results.
"""
boxes = boxes.astype(np.float32)
query_boxes = query_boxes.astype(np.float32)
N = boxes.shape[0]
K = query_boxes.shape[0]
iou = np.zeros((N, K), dtype=np.float32)
if N == 0 or K == 0:
return iou
threadsPerBlock = 8 * 8
cuda.select_device(device_id)
blockspergrid = (div_up(N, threadsPerBlock), div_up(K, threadsPerBlock))
stream = cuda.stream()
with stream.auto_synchronize():
boxes_dev = cuda.to_device(boxes.reshape([-1]), stream)
query_boxes_dev = cuda.to_device(query_boxes.reshape([-1]), stream)
iou_dev = cuda.to_device(iou.reshape([-1]), stream)
rotate_iou_kernel_eval[blockspergrid, threadsPerBlock,
stream](N, K, boxes_dev, query_boxes_dev,
iou_dev, criterion)
iou_dev.copy_to_host(iou.reshape([-1]), stream=stream)
return iou.astype(boxes.dtype)
|