def offset_target_single(flat_anchors,
valid_flags,
bbox_pred,
gt_bboxes,
gt_cheby,
gt_skeleton,
gt_bboxes_ignore,
gt_labels,
img_meta,
target_means,
target_stds,
num_coords,
cfg,
label_channels=1,
sampling=True,
unmap_outputs=True):
inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
img_meta['img_shape'][:2],
cfg.allowed_border)
if not inside_flags.any():
return (None, ) * 6
# assign gt and sample anchors
anchors = flat_anchors[inside_flags, :]
if sampling:
assign_result, sampling_result = assign_and_sample(
anchors, gt_bboxes, gt_cheby, gt_skeleton, gt_bboxes_ignore, None,
cfg)
else:
bbox_assigner = build_assigner(cfg.assigner)
assign_result = bbox_assigner.assign(anchors, gt_bboxes,
gt_bboxes_ignore, gt_labels)
bbox_sampler = PseudoSampler()
sampling_result = bbox_sampler.sample(assign_result, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
labels = anchors.new_zeros(num_valid_anchors, dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
bbox_targets = torch.zeros((num_valid_anchors, num_coords),
dtype=torch.float).cuda()
bbox_weights = torch.zeros((num_valid_anchors, num_coords),
dtype=torch.float).cuda()
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
deltas, weights = bbox2offset(sampling_result.pos_bboxes,
sampling_result.pos_gt_bboxes,
target_means, target_stds)
bbox_targets[pos_inds, :] = deltas
bbox_weights[pos_inds, :] = weights.unsqueeze(
1) if cfg.use_centerness else 1.0
if gt_labels is None:
labels[pos_inds] = 1
else:
labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
if cfg.pos_weight <= 0:
label_weights[pos_inds] = weights if cfg.use_centerness else 1.0
else:
label_weights[pos_inds] = cfg.pos_weight
# start adaptive weights
use_adaptive_weights = False
if use_adaptive_weights:
pos_weights = torch.norm(pos_bbox_targets[:, -2:], dim=1)
pos_weights = 1.0 / (0.5 + pos_weights)
bbox_weights[pos_inds, :] = pos_weights.reshape(-1, 1)
label_weights[pos_inds] = pos_weights
# end of adaptive weights
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_anchors.size(0)
labels = unmap(labels, num_total_anchors, inside_flags)
label_weights = unmap(label_weights, num_total_anchors, inside_flags)
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
neg_inds)
def radius_target_single(flat_anchors,
valid_flags,
bbox_pred,
gt_bboxes,
gt_cheby,
gt_skeleton,
gt_bboxes_ignore,
gt_labels,
img_meta,
target_means,
target_stds,
num_coords,
cfg,
label_channels=1,
sampling=True,
unmap_outputs=True):
inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
img_meta['img_shape'][:2],
cfg.allowed_border)
if not inside_flags.any():
return (None, ) * 6
# assign gt and sample anchors
anchors = flat_anchors[inside_flags, :]
# print('at cheby_target, gt_bboxes_ignore:', gt_bboxes_ignore)
if sampling:
assign_result, sampling_result = assign_and_sample(
anchors, gt_bboxes, gt_cheby, gt_skeleton, gt_bboxes_ignore, None,
cfg)
else:
bbox_assigner = build_assigner(cfg.assigner)
assign_result = bbox_assigner.assign(anchors, gt_bboxes,
gt_bboxes_ignore, gt_labels)
bbox_sampler = PseudoSampler()
sampling_result = bbox_sampler.sample(assign_result, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
labels = anchors.new_zeros(num_valid_anchors, dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
bbox_targets = torch.zeros((num_valid_anchors, num_coords - 3),
dtype=torch.float).cuda()
bbox_weights = torch.zeros((num_valid_anchors, num_coords - 3),
dtype=torch.float).cuda()
ctr_targets = torch.zeros((num_valid_anchors, 3), dtype=torch.float).cuda()
ctr_weights = torch.zeros((num_valid_anchors, 3), dtype=torch.float).cuda()
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
deltas, weights = bbox2radius(sampling_result.pos_bboxes,
sampling_result.pos_gt_bboxes,
sampling_result.pos_gt_skeleton,
num_coords, target_means, target_stds)
bbox_targets[pos_inds, :] = deltas[:, :-3]
bbox_weights[pos_inds, :] = weights.unsqueeze(
1) if cfg.use_centerness else 1.0
ctr_targets[pos_inds, :] = deltas[:, -3:]
ctr_weights[pos_inds, :] = weights.unsqueeze(
1) if cfg.use_centerness else 1.0
if gt_labels is None:
labels[pos_inds] = 1
else:
labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
if cfg.pos_weight <= 0:
label_weights[pos_inds] = weights if cfg.use_centerness else 1.0
else:
label_weights[pos_inds] = cfg.pos_weight
# print("pos:", len(pos_inds), "neg:", len(neg_inds), weights)
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_anchors.size(0)
labels = unmap(labels, num_total_anchors, inside_flags)
label_weights = unmap(label_weights, num_total_anchors, inside_flags)
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
ctr_targets = unmap(ctr_targets, num_total_anchors, inside_flags)
ctr_weights = unmap(ctr_weights, num_total_anchors, inside_flags)
return (labels, label_weights, bbox_targets, bbox_weights, ctr_targets,
ctr_weights, pos_inds, neg_inds)
def _get_target_single(self,
flat_anchors,
valid_flags,
num_level_anchors,
gt_bboxes,
gt_bboxes_ignore,
gt_labels,
img_meta,
label_channels=1,
sampling=True,
unmap_outputs=True):
inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
img_meta['img_shape'][:2],
self.train_cfg.allowed_border)
if not inside_flags.any():
return (None, ) * 6
# assign gt and sample anchors
anchors = flat_anchors[inside_flags, :]
num_level_anchors_inside = self.get_num_level_anchors_inside(
num_level_anchors, inside_flags)
assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
gt_bboxes, gt_bboxes_ignore,
gt_labels)
sampling_result = self.sampler.sample(assign_result, anchors,
gt_bboxes)
num_valid_anchors = anchors.shape[0]
bbox_targets = torch.zeros_like(anchors)
bbox_weights = torch.zeros_like(anchors)
labels = anchors.new_full((num_valid_anchors, ),
self.background_label,
dtype=torch.long)
label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
pos_inds = sampling_result.pos_inds
neg_inds = sampling_result.neg_inds
if len(pos_inds) > 0:
pos_bbox_targets = bbox2delta(sampling_result.pos_bboxes,
sampling_result.pos_gt_bboxes,
self.target_means, self.target_stds)
bbox_targets[pos_inds, :] = pos_bbox_targets
bbox_weights[pos_inds, :] = 1.0
if gt_labels is None:
labels[pos_inds] = 1
else:
labels[pos_inds] = gt_labels[
sampling_result.pos_assigned_gt_inds]
if self.train_cfg.pos_weight <= 0:
label_weights[pos_inds] = 1.0
else:
label_weights[pos_inds] = self.train_cfg.pos_weight
if len(neg_inds) > 0:
label_weights[neg_inds] = 1.0
# map up to original set of anchors
if unmap_outputs:
num_total_anchors = flat_anchors.size(0)
anchors = unmap(anchors, num_total_anchors, inside_flags)
labels = unmap(labels,
num_total_anchors,
inside_flags,
fill=self.num_classes)
label_weights = unmap(label_weights, num_total_anchors,
inside_flags)
bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
return (anchors, labels, label_weights, bbox_targets, bbox_weights,
pos_inds, neg_inds)