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import mmcv
import torch
from mmdet.core import bbox_overlaps
@mmcv.jit(derivate=True, coderize=True)
def isr_p(cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
loss_cls,
bbox_coder,
k=2,
bias=0,
num_class=80):
"""Importance-based Sample Reweighting (ISR_P), positive part.
Args:
cls_score (Tensor): Predicted classification scores.
bbox_pred (Tensor): Predicted bbox deltas.
bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are
labels, label_weights, bbox_targets, bbox_weights, respectively.
rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs
(two_stage) in shape (n, 5).
sampling_results (obj): Sampling results.
loss_cls (func): Classification loss func of the head.
bbox_coder (obj): BBox coder of the head.
k (float): Power of the non-linear mapping.
bias (float): Shift of the non-linear mapping.
num_class (int): Number of classes, default: 80.
Return:
tuple([Tensor]): labels, imp_based_label_weights, bbox_targets,
bbox_target_weights
"""
labels, label_weights, bbox_targets, bbox_weights = bbox_targets
pos_label_inds = ((labels >= 0) &
(labels < num_class)).nonzero().reshape(-1)
pos_labels = labels[pos_label_inds]
# if no positive samples, return the original targets
num_pos = float(pos_label_inds.size(0))
if num_pos == 0:
return labels, label_weights, bbox_targets, bbox_weights
# merge pos_assigned_gt_inds of per image to a single tensor
gts = list()
last_max_gt = 0
for i in range(len(sampling_results)):
gt_i = sampling_results[i].pos_assigned_gt_inds
gts.append(gt_i + last_max_gt)
if len(gt_i) != 0:
last_max_gt = gt_i.max() + 1
gts = torch.cat(gts)
assert len(gts) == num_pos
cls_score = cls_score.detach()
bbox_pred = bbox_pred.detach()
# For single stage detectors, rois here indicate anchors, in shape (N, 4)
# For two stage detectors, rois are in shape (N, 5)
if rois.size(-1) == 5:
pos_rois = rois[pos_label_inds][:, 1:]
else:
pos_rois = rois[pos_label_inds]
if bbox_pred.size(-1) > 4:
bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4)
else:
pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4)
# compute iou of the predicted bbox and the corresponding GT
pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4)
pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred)
target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target)
ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True)
pos_imp_weights = label_weights[pos_label_inds]
# Two steps to compute IoU-HLR. Samples are first sorted by IoU locally,
# then sorted again within the same-rank group
max_l_num = pos_labels.bincount().max()
for label in pos_labels.unique():
l_inds = (pos_labels == label).nonzero().view(-1)
l_gts = gts[l_inds]
for t in l_gts.unique():
t_inds = l_inds[l_gts == t]
t_ious = ious[t_inds]
_, t_iou_rank_idx = t_ious.sort(descending=True)
_, t_iou_rank = t_iou_rank_idx.sort()
ious[t_inds] += max_l_num - t_iou_rank.float()
l_ious = ious[l_inds]
_, l_iou_rank_idx = l_ious.sort(descending=True)
_, l_iou_rank = l_iou_rank_idx.sort() # IoU-HLR
# linearly map HLR to label weights
pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num
pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k)
# normalize to make the new weighted loss value equal to the original loss
pos_loss_cls = loss_cls(
cls_score[pos_label_inds], pos_labels, reduction_override='none')
if pos_loss_cls.dim() > 1:
ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:,
None]
new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None]
else:
ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds]
new_pos_loss_cls = pos_loss_cls * pos_imp_weights
pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum()
pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio
label_weights[pos_label_inds] = pos_imp_weights
bbox_targets = labels, label_weights, bbox_targets, bbox_weights
return bbox_targets
@mmcv.jit(derivate=True, coderize=True)
def carl_loss(cls_score,
labels,
bbox_pred,
bbox_targets,
loss_bbox,
k=1,
bias=0.2,
avg_factor=None,
sigmoid=False,
num_class=80):
"""Classification-Aware Regression Loss (CARL).
Args:
cls_score (Tensor): Predicted classification scores.
labels (Tensor): Targets of classification.
bbox_pred (Tensor): Predicted bbox deltas.
bbox_targets (Tensor): Target of bbox regression.
loss_bbox (func): Regression loss func of the head.
bbox_coder (obj): BBox coder of the head.
k (float): Power of the non-linear mapping.
bias (float): Shift of the non-linear mapping.
avg_factor (int): Average factor used in regression loss.
sigmoid (bool): Activation of the classification score.
num_class (int): Number of classes, default: 80.
Return:
dict: CARL loss dict.
"""
pos_label_inds = ((labels >= 0) &
(labels < num_class)).nonzero().reshape(-1)
if pos_label_inds.numel() == 0:
return dict(loss_carl=cls_score.sum()[None] * 0.)
pos_labels = labels[pos_label_inds]
# multiply pos_cls_score with the corresponding bbox weight
# and remain gradient
if sigmoid:
pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]
else:
pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]
carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)
# normalize carl_loss_weight to make its sum equal to num positive
num_pos = float(pos_cls_score.size(0))
weight_ratio = num_pos / carl_loss_weights.sum()
carl_loss_weights *= weight_ratio
if avg_factor is None:
avg_factor = bbox_targets.size(0)
# if is class agnostic, bbox pred is in shape (N, 4)
# otherwise, bbox pred is in shape (N, #classes, 4)
if bbox_pred.size(-1) > 4:
bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]
else:
pos_bbox_preds = bbox_pred[pos_label_inds]
ori_loss_reg = loss_bbox(
pos_bbox_preds,
bbox_targets[pos_label_inds],
reduction_override='none') / avg_factor
loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()
return dict(loss_carl=loss_carl[None])
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