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# 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)
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