def aux_loss(pts_labels, center_targets, point_cls, point_reg): # 附加任务的损失函数 r''' pts_labels = sample_seg_label, B * 512 center_targets = sample_seg_offset, B * 512 * 3 point_cls = estimation_seg, B * 512 point_reg = estimation_offset, B * 512 * 3 ''' N = len(pts_labels) rpn_cls_target = pts_labels.float() pos = (pts_labels > 0).float() neg = (pts_labels == 0).float() pos_normalizer = pos.sum() pos_normalizer = torch.clamp(pos_normalizer, min=1.0) cls_weights = pos + neg cls_weights = cls_weights / pos_normalizer reg_weights = pos reg_weights = reg_weights / pos_normalizer aux_loss_cls = weighted_sigmoid_focal_loss( point_cls, rpn_cls_target, weight=cls_weights, avg_factor=1.) aux_loss_cls /= N aux_loss_reg = weighted_smoothl1( point_reg, center_targets, beta=1 / 9., weight=reg_weights[..., None], avg_factor=1.) aux_loss_reg /= N return aux_loss_cls, aux_loss_reg
def create_loss(self,
box_preds,
cls_preds,
cls_targets,
cls_weights,
reg_targets,
reg_weights,
num_class,
use_sigmoid_cls=True,
encode_rad_error_by_sin=True,
box_code_size=7):
batch_size = int(box_preds.shape[0])
box_preds = box_preds.view(batch_size, -1, box_code_size)
if use_sigmoid_cls:
cls_preds = cls_preds.view(batch_size, -1, num_class)
else:
cls_preds = cls_preds.view(batch_size, -1, num_class + 1)
one_hot_targets = one_hot(
cls_targets, depth=num_class + 1, dtype=box_preds.dtype)
if use_sigmoid_cls:
one_hot_targets = one_hot_targets[..., 1:]
if encode_rad_error_by_sin:
# sin(a - b) = sinacosb-cosasinb
box_preds, reg_targets = self.add_sin_difference(box_preds, reg_targets)
loc_losses = weighted_smoothl1(box_preds, reg_targets, beta=1 / 9., \
weight=reg_weights[..., None], avg_factor=1.)
cls_losses = weighted_sigmoid_focal_loss(cls_preds, one_hot_targets, \
weight=cls_weights[..., None], avg_factor=1.)
return loc_losses, cls_losses
def aux_loss(points, point_cls, point_reg, gt_bboxes):
N = len(gt_bboxes)
pts_labels, center_targets = build_aux_target(points[:, :4], gt_bboxes)
rpn_cls_target = pts_labels.float()
pos = (pts_labels > 0).float()
neg = (pts_labels == 0).float()
pos_normalizer = pos.sum()
pos_normalizer = torch.clamp(pos_normalizer, min=1.0)
cls_weights = pos + neg
cls_weights = cls_weights / pos_normalizer
reg_weights = pos
reg_weights = reg_weights / pos_normalizer
aux_loss_cls = weighted_sigmoid_focal_loss(point_cls.view(-1),
rpn_cls_target,
weight=cls_weights,
avg_factor=1.)
aux_loss_cls /= N
aux_loss_reg = weighted_smoothl1(point_reg,
center_targets,
beta=1 / 9.,
weight=reg_weights[..., None],
avg_factor=1.)
aux_loss_reg /= N
return dict(
aux_loss_cls=[aux_loss_cls],
aux_loss_reg=[aux_loss_reg],
)
def loss(self, cls_preds, gt_bboxes, gt_labels, anchors, cfg):
batch_size = len(anchors)
batch_none = (None, ) * batch_size
# currently only support rescoring for class agnostic anchors
labels, targets, ious = multi_apply(
create_target_torch,
anchors,
batch_none,
gt_bboxes,
batch_none,
batch_none,
similarity_fn=getattr(iou3d_utils, cfg.assigner.similarity_fn)(),
box_encoding_fn=second_box_encode,
matched_threshold=cfg.assigner.pos_iou_thr,
unmatched_threshold=cfg.assigner.neg_iou_thr)
labels = torch.cat(labels, ).unsqueeze_(1)
# soft_label = torch.clamp(2 * ious - 0.5, 0, 1)
# labels = soft_label * labels.float()
cared = labels >= 0
positives = labels > 0
negatives = labels == 0
negative_cls_weights = negatives.type(torch.float32)
cls_weights = negative_cls_weights + positives.type(torch.float32)
pos_normalizer = positives.sum().type(torch.float32)
cls_weights /= torch.clamp(pos_normalizer, min=1.0)
cls_targets = labels * cared.type_as(labels)
cls_preds = cls_preds.view(-1, self._num_class)
cls_losses = weighted_sigmoid_focal_loss(cls_preds, cls_targets.float(), \
weight=cls_weights, avg_factor=1.)
cls_loss_reduced = cls_losses / batch_size
return dict(loss_cls=cls_loss_reduced, )
