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import torch
from ..builder import BBOX_ASSIGNERS
from ..match_costs import build_match_cost
from ..transforms import bbox_cxcywh_to_xyxy
from .assign_result import AssignResult
from .base_assigner import BaseAssigner
try:
from scipy.optimize import linear_sum_assignment
except ImportError:
linear_sum_assignment = None
@BBOX_ASSIGNERS.register_module()
class HungarianAssigner(BaseAssigner):
"""Computes one-to-one matching between predictions and ground truth.
This class computes an assignment between the targets and the predictions
based on the costs. The costs are weighted sum of three components:
classification cost, regression L1 cost and regression iou cost. The
targets don't include the no_object, so generally there are more
predictions than targets. After the one-to-one matching, the un-matched
are treated as backgrounds. Thus each query prediction will be assigned
with `0` or a positive integer indicating the ground truth index:
- 0: negative sample, no assigned gt
- positive integer: positive sample, index (1-based) of assigned gt
Args:
cls_weight (int | float, optional): The scale factor for classification
cost. Default 1.0.
bbox_weight (int | float, optional): The scale factor for regression
L1 cost. Default 1.0.
iou_weight (int | float, optional): The scale factor for regression
iou cost. Default 1.0.
iou_calculator (dict | optional): The config for the iou calculation.
Default type `BboxOverlaps2D`.
iou_mode (str | optional): "iou" (intersection over union), "iof"
(intersection over foreground), or "giou" (generalized
intersection over union). Default "giou".
"""
def __init__(self,
cls_cost=dict(type='ClassificationCost', weight=1.),
reg_cost=dict(type='BBoxL1Cost', weight=1.0),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0)):
self.cls_cost = build_match_cost(cls_cost)
self.reg_cost = build_match_cost(reg_cost)
self.iou_cost = build_match_cost(iou_cost)
def assign(self,
bbox_pred,
cls_pred,
gt_bboxes,
gt_labels,
img_meta,
gt_bboxes_ignore=None,
eps=1e-7):
"""Computes one-to-one matching based on the weighted costs.
This method assign each query prediction to a ground truth or
background. The `assigned_gt_inds` with -1 means don't care,
0 means negative sample, and positive number is the index (1-based)
of assigned gt.
The assignment is done in the following steps, the order matters.
1. assign every prediction to -1
2. compute the weighted costs
3. do Hungarian matching on CPU based on the costs
4. assign all to 0 (background) first, then for each matched pair
between predictions and gts, treat this prediction as foreground
and assign the corresponding gt index (plus 1) to it.
Args:
bbox_pred (Tensor): Predicted boxes with normalized coordinates
(cx, cy, w, h), which are all in range [0, 1]. Shape
[num_query, 4].
cls_pred (Tensor): Predicted classification logits, shape
[num_query, num_class].
gt_bboxes (Tensor): Ground truth boxes with unnormalized
coordinates (x1, y1, x2, y2). Shape [num_gt, 4].
gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
img_meta (dict): Meta information for current image.
gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
labelled as `ignored`. Default None.
eps (int | float, optional): A value added to the denominator for
numerical stability. Default 1e-7.
Returns:
:obj:`AssignResult`: The assigned result.
"""
assert gt_bboxes_ignore is None, \
'Only case when gt_bboxes_ignore is None is supported.'
num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0)
# 1. assign -1 by default
assigned_gt_inds = bbox_pred.new_full((num_bboxes, ),
-1,
dtype=torch.long)
assigned_labels = bbox_pred.new_full((num_bboxes, ),
-1,
dtype=torch.long)
if num_gts == 0 or num_bboxes == 0:
# No ground truth or boxes, return empty assignment
if num_gts == 0:
# No ground truth, assign all to background
assigned_gt_inds[:] = 0
return AssignResult(
num_gts, assigned_gt_inds, None, labels=assigned_labels)
img_h, img_w, _ = img_meta['img_shape']
factor = gt_bboxes.new_tensor([img_w, img_h, img_w,
img_h]).unsqueeze(0)
# 2. compute the weighted costs
# classification and bboxcost.
cls_cost = self.cls_cost(cls_pred, gt_labels)
# regression L1 cost
normalize_gt_bboxes = gt_bboxes / factor
reg_cost = self.reg_cost(bbox_pred, normalize_gt_bboxes)
# regression iou cost, defaultly giou is used in official DETR.
bboxes = bbox_cxcywh_to_xyxy(bbox_pred) * factor
iou_cost = self.iou_cost(bboxes, gt_bboxes)
# weighted sum of above three costs
cost = cls_cost + reg_cost + iou_cost
# 3. do Hungarian matching on CPU using linear_sum_assignment
cost = cost.detach().cpu()
if linear_sum_assignment is None:
raise ImportError('Please run "pip install scipy" '
'to install scipy first.')
matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
matched_row_inds = torch.from_numpy(matched_row_inds).to(
bbox_pred.device)
matched_col_inds = torch.from_numpy(matched_col_inds).to(
bbox_pred.device)
# 4. assign backgrounds and foregrounds
# assign all indices to backgrounds first
assigned_gt_inds[:] = 0
# assign foregrounds based on matching results
assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
return AssignResult(
num_gts, assigned_gt_inds, None, labels=assigned_labels)
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