import torch
from mmdet3d.core import bbox3d2result, merge_aug_bboxes_3d
from mmdet.models import DETECTORS
from .single_stage import SingleStage3DDetector
[docs]@DETECTORS.register_module()
class VoteNet(SingleStage3DDetector):
r"""`VoteNet <https://arxiv.org/pdf/1904.09664.pdf>`_ for 3D detection."""
def __init__(self,
backbone,
bbox_head=None,
train_cfg=None,
test_cfg=None,
init_cfg=None,
pretrained=None):
super(VoteNet, self).__init__(
backbone=backbone,
bbox_head=bbox_head,
train_cfg=train_cfg,
test_cfg=test_cfg,
init_cfg=None,
pretrained=pretrained)
[docs] def forward_train(self,
points,
img_metas,
gt_bboxes_3d,
gt_labels_3d,
pts_semantic_mask=None,
pts_instance_mask=None,
gt_bboxes_ignore=None):
"""Forward of training.
Args:
points (list[torch.Tensor]): Points of each batch.
img_metas (list): Image metas.
gt_bboxes_3d (:obj:`BaseInstance3DBoxes`): gt bboxes of each batch.
gt_labels_3d (list[torch.Tensor]): gt class labels of each batch.
pts_semantic_mask (None | list[torch.Tensor]): point-wise semantic
label of each batch.
pts_instance_mask (None | list[torch.Tensor]): point-wise instance
label of each batch.
gt_bboxes_ignore (None | list[torch.Tensor]): Specify
which bounding.
Returns:
dict: Losses.
"""
points_cat = torch.stack(points)
x = self.extract_feat(points_cat)
bbox_preds = self.bbox_head(x, self.train_cfg.sample_mod)
loss_inputs = (points, gt_bboxes_3d, gt_labels_3d, pts_semantic_mask,
pts_instance_mask, img_metas)
losses = self.bbox_head.loss(
bbox_preds, *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
[docs] def simple_test(self, points, img_metas, imgs=None, rescale=False):
"""Forward of testing.
Args:
points (list[torch.Tensor]): Points of each sample.
img_metas (list): Image metas.
rescale (bool): Whether to rescale results.
Returns:
list: Predicted 3d boxes.
"""
points_cat = torch.stack(points)
x = self.extract_feat(points_cat)
bbox_preds = self.bbox_head(x, self.test_cfg.sample_mod)
bbox_list = self.bbox_head.get_bboxes(
points_cat, bbox_preds, img_metas, rescale=rescale)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results
[docs] def aug_test(self, points, img_metas, imgs=None, rescale=False):
"""Test with augmentation."""
points_cat = [torch.stack(pts) for pts in points]
feats = self.extract_feats(points_cat, img_metas)
# only support aug_test for one sample
aug_bboxes = []
for x, pts_cat, img_meta in zip(feats, points_cat, img_metas):
bbox_preds = self.bbox_head(x, self.test_cfg.sample_mod)
bbox_list = self.bbox_head.get_bboxes(
pts_cat, bbox_preds, img_meta, rescale=rescale)
bbox_list = [
dict(boxes_3d=bboxes, scores_3d=scores, labels_3d=labels)
for bboxes, scores, labels in bbox_list
]
aug_bboxes.append(bbox_list[0])
# after merging, bboxes will be rescaled to the original image size
merged_bboxes = merge_aug_bboxes_3d(aug_bboxes, img_metas,
self.bbox_head.test_cfg)
return [merged_bboxes]