def get_target(self, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
reg_classes = 1 if self.reg_class_agnostic else self.num_classes
cls_reg_targets = bbox_target(pos_proposals,
neg_proposals,
pos_gt_bboxes,
pos_gt_labels,
rcnn_train_cfg,
reg_classes,
target_means=self.target_means,
target_stds=self.target_stds)
return cls_reg_targets
def get_target(self, sampling_results, gt_bboxes, gt_labels,
rcnn_train_cfg):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_bboxes_3d = [res.pos_gt_bboxes_3d for res in sampling_results]
# print(pos_gt_bboxes[0].size(), pos_gt_bboxes_3d[0].size())
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
reg_classes = 1 if self.reg_class_agnostic else self.num_classes
# from mmdet.apis import get_root_logger
# logger = get_root_logger()
# logger.info('pos_gt_bboxes{}'.format(pos_gt_bboxes))
# logger.info('pos_gt_bboxes_3d{}'.format(pos_gt_bboxes_3d[:2])) # [[ 5.5125e-01, 9.7504e-01, -5.9984e-01, 3.7557e+02, 3.5705e+02, -1.3744e+00, 8.6807e-01, 1.0626e+00],
cls_reg_targets = bbox_target(pos_proposals,
neg_proposals,
pos_gt_bboxes,
pos_gt_labels,
rcnn_train_cfg,
reg_classes,
target_means=self.target_means,
target_stds=self.target_stds,
pos_gt_bboxes_3d=pos_gt_bboxes_3d)
# logger.info('cls_reg_targets{}'.format(cls_reg_targets[3][:2]))
return cls_reg_targets
def get_target(self,
sampling_results,
gt_bboxes,
gt_labels,
rcnn_train_cfg,
img_metas=None):
pos_proposals = [res.pos_bboxes for res in sampling_results]
neg_proposals = [res.neg_bboxes for res in sampling_results]
pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
reg_classes = 1 if self.reg_class_agnostic else self.num_classes
labels, label_weights, bbox_targets, bbox_weights = bbox_target(
pos_proposals,
neg_proposals,
pos_gt_bboxes,
pos_gt_labels,
rcnn_train_cfg,
reg_classes,
target_means=self.target_means,
target_stds=self.target_stds,
concat=self.concat_targets)
labels, label_weights, target_meta = process_class_label(
labels,
label_weights,
img_metas,
concat_targets=self.concat_targets,
sparse_label=self.sparse_label,
graph=self.graph,
use_sigmoid_cls=self.use_sigmoid_cls,
eql_cfg=self.eql_cfg,
num_classes=self.num_classes)
if not self.concat_targets:
bbox_targets = torch.cat(bbox_targets)
bbox_weights = torch.cat(bbox_weights)
'''
if self.sparse_label:
target_meta = {'labels': _labels}
bin_labels, bin_label_weights = _expand_binary_labels(
_labels, _label_weights, self.num_classes)
if not self.propagate_labels:
# targets for sigmoid activation
return bin_labels, bin_label_weights, bbox_targets, bbox_weights, target_meta # noqa
# propagate on graph
assert self.graph is not None
pos_inds = _labels > 0
neg_inds = _labels == 0
label_weights = bin_labels.new_zeros(
bin_labels.size(), dtype=torch.float)
# label weights for softmax loss formulation.
label_weights[pos_inds, 1:] = torch.matmul(
bin_labels[pos_inds, 1:].float(), self.graph)
label_weights[neg_inds, 0] = 1.0
labels = (label_weights.clone() > 0).long()
if self.use_sigmoid_cls:
label_weights = label_weights.new_ones(
label_weights.size(), dtype=torch.float)
# label weights: further processing
return labels, label_weights, bbox_targets, bbox_weights, target_meta # noqa
# categorical.
'''
return labels, label_weights, bbox_targets, bbox_weights, target_meta