region_assigner.py
9.47 KB
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
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import anchor_inside_flags
from ..builder import BBOX_ASSIGNERS
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
def calc_region(bbox, ratio, stride, featmap_size=None):
"""Calculate region of the box defined by the ratio, the ratio is from the
center of the box to every edge."""
# project bbox on the feature
f_bbox = bbox / stride
x1 = torch.round((1 - ratio) * f_bbox[0] + ratio * f_bbox[2])
y1 = torch.round((1 - ratio) * f_bbox[1] + ratio * f_bbox[3])
x2 = torch.round(ratio * f_bbox[0] + (1 - ratio) * f_bbox[2])
y2 = torch.round(ratio * f_bbox[1] + (1 - ratio) * f_bbox[3])
if featmap_size is not None:
x1 = x1.clamp(min=0, max=featmap_size[1])
y1 = y1.clamp(min=0, max=featmap_size[0])
x2 = x2.clamp(min=0, max=featmap_size[1])
y2 = y2.clamp(min=0, max=featmap_size[0])
return (x1, y1, x2, y2)
def anchor_ctr_inside_region_flags(anchors, stride, region):
"""Get the flag indicate whether anchor centers are inside regions."""
x1, y1, x2, y2 = region
f_anchors = anchors / stride
x = (f_anchors[:, 0] + f_anchors[:, 2]) * 0.5
y = (f_anchors[:, 1] + f_anchors[:, 3]) * 0.5
flags = (x >= x1) & (x <= x2) & (y >= y1) & (y <= y2)
return flags
@BBOX_ASSIGNERS.register_module()
class RegionAssigner(BaseAssigner):
"""Assign a corresponding gt bbox or background to each bbox.
Each proposals will be assigned with `-1`, `0`, or a positive integer
indicating the ground truth index.
- -1: don't care
- 0: negative sample, no assigned gt
- positive integer: positive sample, index (1-based) of assigned gt
Args:
center_ratio: ratio of the region in the center of the bbox to
define positive sample.
ignore_ratio: ratio of the region to define ignore samples.
"""
def __init__(self, center_ratio=0.2, ignore_ratio=0.5):
self.center_ratio = center_ratio
self.ignore_ratio = ignore_ratio
def assign(self,
mlvl_anchors,
mlvl_valid_flags,
gt_bboxes,
img_meta,
featmap_sizes,
anchor_scale,
anchor_strides,
gt_bboxes_ignore=None,
gt_labels=None,
allowed_border=0):
"""Assign gt to anchors.
This method assign a gt bbox to every bbox (proposal/anchor), each bbox
will be assigned with -1, 0, or a positive number. -1 means don't care,
0 means negative sample, positive number is the index (1-based) of
assigned gt.
The assignment is done in following steps, and the order matters.
1. Assign every anchor to 0 (negative)
2. (For each gt_bboxes) Compute ignore flags based on ignore_region
then assign -1 to anchors w.r.t. ignore flags
3. (For each gt_bboxes) Compute pos flags based on center_region then
assign gt_bboxes to anchors w.r.t. pos flags
4. (For each gt_bboxes) Compute ignore flags based on adjacent anchor
level then assign -1 to anchors w.r.t. ignore flags
5. Assign anchor outside of image to -1
Args:
mlvl_anchors (list[Tensor]): Multi level anchors.
mlvl_valid_flags (list[Tensor]): Multi level valid flags.
gt_bboxes (Tensor): Ground truth bboxes of image
img_meta (dict): Meta info of image.
featmap_sizes (list[Tensor]): Feature mapsize each level
anchor_scale (int): Scale of the anchor.
anchor_strides (list[int]): Stride of the anchor.
gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
labelled as `ignored`, e.g., crowd boxes in COCO.
gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
allowed_border (int, optional): The border to allow the valid
anchor. Defaults to 0.
Returns:
:obj:`AssignResult`: The assign result.
"""
if gt_bboxes_ignore is not None:
raise NotImplementedError
num_gts = gt_bboxes.shape[0]
num_bboxes = sum(x.shape[0] for x in mlvl_anchors)
if num_gts == 0 or num_bboxes == 0:
# No ground truth or boxes, return empty assignment
max_overlaps = gt_bboxes.new_zeros((num_bboxes, ))
assigned_gt_inds = gt_bboxes.new_zeros((num_bboxes, ),
dtype=torch.long)
if gt_labels is None:
assigned_labels = None
else:
assigned_labels = gt_bboxes.new_full((num_bboxes, ),
-1,
dtype=torch.long)
return AssignResult(
num_gts,
assigned_gt_inds,
max_overlaps,
labels=assigned_labels)
num_lvls = len(mlvl_anchors)
r1 = (1 - self.center_ratio) / 2
r2 = (1 - self.ignore_ratio) / 2
scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
(gt_bboxes[:, 3] - gt_bboxes[:, 1]))
min_anchor_size = scale.new_full(
(1, ), float(anchor_scale * anchor_strides[0]))
target_lvls = torch.floor(
torch.log2(scale) - torch.log2(min_anchor_size) + 0.5)
target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long()
# 1. assign 0 (negative) by default
mlvl_assigned_gt_inds = []
mlvl_ignore_flags = []
for lvl in range(num_lvls):
h, w = featmap_sizes[lvl]
assert h * w == mlvl_anchors[lvl].shape[0]
assigned_gt_inds = gt_bboxes.new_full((h * w, ),
0,
dtype=torch.long)
ignore_flags = torch.zeros_like(assigned_gt_inds)
mlvl_assigned_gt_inds.append(assigned_gt_inds)
mlvl_ignore_flags.append(ignore_flags)
for gt_id in range(num_gts):
lvl = target_lvls[gt_id].item()
featmap_size = featmap_sizes[lvl]
stride = anchor_strides[lvl]
anchors = mlvl_anchors[lvl]
gt_bbox = gt_bboxes[gt_id, :4]
# Compute regions
ignore_region = calc_region(gt_bbox, r2, stride, featmap_size)
ctr_region = calc_region(gt_bbox, r1, stride, featmap_size)
# 2. Assign -1 to ignore flags
ignore_flags = anchor_ctr_inside_region_flags(
anchors, stride, ignore_region)
mlvl_assigned_gt_inds[lvl][ignore_flags] = -1
# 3. Assign gt_bboxes to pos flags
pos_flags = anchor_ctr_inside_region_flags(anchors, stride,
ctr_region)
mlvl_assigned_gt_inds[lvl][pos_flags] = gt_id + 1
# 4. Assign -1 to ignore adjacent lvl
if lvl > 0:
d_lvl = lvl - 1
d_anchors = mlvl_anchors[d_lvl]
d_featmap_size = featmap_sizes[d_lvl]
d_stride = anchor_strides[d_lvl]
d_ignore_region = calc_region(gt_bbox, r2, d_stride,
d_featmap_size)
ignore_flags = anchor_ctr_inside_region_flags(
d_anchors, d_stride, d_ignore_region)
mlvl_ignore_flags[d_lvl][ignore_flags] = 1
if lvl < num_lvls - 1:
u_lvl = lvl + 1
u_anchors = mlvl_anchors[u_lvl]
u_featmap_size = featmap_sizes[u_lvl]
u_stride = anchor_strides[u_lvl]
u_ignore_region = calc_region(gt_bbox, r2, u_stride,
u_featmap_size)
ignore_flags = anchor_ctr_inside_region_flags(
u_anchors, u_stride, u_ignore_region)
mlvl_ignore_flags[u_lvl][ignore_flags] = 1
# 4. (cont.) Assign -1 to ignore adjacent lvl
for lvl in range(num_lvls):
ignore_flags = mlvl_ignore_flags[lvl]
mlvl_assigned_gt_inds[lvl][ignore_flags] = -1
# 5. Assign -1 to anchor outside of image
flat_assigned_gt_inds = torch.cat(mlvl_assigned_gt_inds)
flat_anchors = torch.cat(mlvl_anchors)
flat_valid_flags = torch.cat(mlvl_valid_flags)
assert (flat_assigned_gt_inds.shape[0] == flat_anchors.shape[0] ==
flat_valid_flags.shape[0])
inside_flags = anchor_inside_flags(flat_anchors, flat_valid_flags,
img_meta['img_shape'],
allowed_border)
outside_flags = ~inside_flags
flat_assigned_gt_inds[outside_flags] = -1
if gt_labels is not None:
assigned_labels = torch.zeros_like(flat_assigned_gt_inds)
pos_flags = assigned_gt_inds > 0
assigned_labels[pos_flags] = gt_labels[
flat_assigned_gt_inds[pos_flags] - 1]
else:
assigned_labels = None
return AssignResult(
num_gts, flat_assigned_gt_inds, None, labels=assigned_labels)