def get_det_bboxes(self,
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None):
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
# TODO: check and simplify it
if rois.size(1) == 5:
obbs = hbb2obb_v2(rois[:, 1:])
elif rois.size(1) == 6:
obbs = rois[:, 1:]
else:
print('strange size')
import pdb
pdb.set_trace()
if bbox_pred is not None:
# bboxes = delta2dbbox(rois[:, 1:], bbox_pred, self.target_means,
# self.target_stds, img_shape)
if self.with_module:
dbboxes = delta2dbbox(obbs, bbox_pred, self.target_means,
self.target_stds, img_shape)
else:
dbboxes = delta2dbbox_v3(obbs, bbox_pred, self.target_means,
self.target_stds, img_shape)
else:
# bboxes = rois[:, 1:]
dbboxes = obbs
# TODO: add clip here
if rescale:
# bboxes /= scale_factor
# dbboxes[:, :4] /= scale_factor
dbboxes[:, 0::5] /= scale_factor
dbboxes[:, 1::5] /= scale_factor
dbboxes[:, 2::5] /= scale_factor
dbboxes[:, 3::5] /= scale_factor
# if cfg is None:
# c_device = dbboxes.device
#
# det_bboxes, det_labels = Pesudomulticlass_nms_rbbox(dbboxes, scores,
# 0.05,
# 1000)
#
# return det_bboxes, det_labels
# else:
c_device = dbboxes.device
det_bboxes, det_labels = multiclass_nms_rbbox(dbboxes, scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
# det_bboxes = torch.from_numpy(det_bboxes).to(c_device)
# det_labels = torch.from_numpy(det_labels).to(c_device)
return det_bboxes, det_labels
def get_det_bboxes(self,
rois,
cls_score,
bbox_pred,
img_shape,
scale_factor,
rescale=False,
cfg=None):
if isinstance(cls_score, list):
cls_score = sum(cls_score) / float(len(cls_score))
scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
# TODO: check and simplify it
if rois.size(1) == 5:
obbs = hbb2obb_v2(rois[:, 1:])
elif rois.size(1) == 6:
obbs = rois[:, 1:]
else:
print('strange size')
import pdb
pdb.set_trace()
if bbox_pred is not None:
# bboxes = delta2dbbox(rois[:, 1:], bbox_pred, self.target_means,
# self.target_stds, img_shape)
if self.with_module:
dbboxes = delta2dbbox(obbs, bbox_pred, self.target_means,
self.target_stds, img_shape)
else:
dbboxes = delta2dbbox_v3(obbs, bbox_pred, self.target_means,
self.target_stds, img_shape)
else:
# bboxes = rois[:, 1:]
dbboxes = obbs
# TODO: add clip here
if rescale:
# bboxes /= scale_factor
# dbboxes[:, :4] /= scale_factor
dbboxes[:, 0::5] /= scale_factor
dbboxes[:, 1::5] /= scale_factor
dbboxes[:, 2::5] /= scale_factor
dbboxes[:, 3::5] /= scale_factor
# if cfg is None:
# c_device = dbboxes.device
#
# det_bboxes, det_labels = Pesudomulticlass_nms_rbbox(dbboxes, scores,
# 0.05,
# 1000)
#
# return det_bboxes, det_labels
# else:
c_device = dbboxes.device
det_bboxes, det_labels = multiclass_nms_rbbox(dbboxes, scores,
cfg.score_thr, cfg.nms,
cfg.max_per_img)
# det_bboxes = torch.from_numpy(det_bboxes).to(c_device)
# det_labels = torch.from_numpy(det_labels).to(c_device)
# ###########################################################
from mmdet.MARK import PRINT_RBBOX_HEAD_RS_LOSS
if PRINT_RBBOX_HEAD_RS_LOSS:
# pos_inds = pos_inds[0:min(len(pos_inds), 10)]
pred_score = scores
pred_score, pred_label = torch.max(pred_score, dim=1)
# 前景标签
pred_f_indices = pred_label != 0
if torch.sum(pred_f_indices) > 0:
print('#' * 80)
print('for pred score: ', pred_score[pred_f_indices])
print('#' * 80)
# ###########################################################
return det_bboxes, det_labels