import mmcv
import torch
import warnings
from mmcv.parallel import DataContainer as DC
from mmcv.runner import force_fp32
from os import path as osp
from torch.nn import functional as F
from mmdet3d.core import (Box3DMode, Coord3DMode, bbox3d2result,
merge_aug_bboxes_3d, show_result)
from mmdet3d.ops import Voxelization
from mmdet.core import multi_apply
from mmdet.models import DETECTORS
from .. import builder
from .base import Base3DDetector
[docs]@DETECTORS.register_module()
class MVXTwoStageDetector(Base3DDetector):
"""Base class of Multi-modality VoxelNet."""
def __init__(self,
pts_voxel_layer=None,
pts_voxel_encoder=None,
pts_middle_encoder=None,
pts_fusion_layer=None,
img_backbone=None,
pts_backbone=None,
img_neck=None,
pts_neck=None,
pts_bbox_head=None,
img_roi_head=None,
img_rpn_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
init_cfg=None):
super(MVXTwoStageDetector, self).__init__(init_cfg=init_cfg)
if pts_voxel_layer:
self.pts_voxel_layer = Voxelization(**pts_voxel_layer)
if pts_voxel_encoder:
self.pts_voxel_encoder = builder.build_voxel_encoder(
pts_voxel_encoder)
if pts_middle_encoder:
self.pts_middle_encoder = builder.build_middle_encoder(
pts_middle_encoder)
if pts_backbone:
self.pts_backbone = builder.build_backbone(pts_backbone)
if pts_fusion_layer:
self.pts_fusion_layer = builder.build_fusion_layer(
pts_fusion_layer)
if pts_neck is not None:
self.pts_neck = builder.build_neck(pts_neck)
if pts_bbox_head:
pts_train_cfg = train_cfg.pts if train_cfg else None
pts_bbox_head.update(train_cfg=pts_train_cfg)
pts_test_cfg = test_cfg.pts if test_cfg else None
pts_bbox_head.update(test_cfg=pts_test_cfg)
self.pts_bbox_head = builder.build_head(pts_bbox_head)
if img_backbone:
self.img_backbone = builder.build_backbone(img_backbone)
if img_neck is not None:
self.img_neck = builder.build_neck(img_neck)
if img_rpn_head is not None:
self.img_rpn_head = builder.build_head(img_rpn_head)
if img_roi_head is not None:
self.img_roi_head = builder.build_head(img_roi_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if pretrained is None:
img_pretrained = None
pts_pretrained = None
elif isinstance(pretrained, dict):
img_pretrained = pretrained.get('img', None)
pts_pretrained = pretrained.get('pts', None)
else:
raise ValueError(
f'pretrained should be a dict, got {type(pretrained)}')
if self.with_img_backbone:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
self.img_backbone.init_cfg = dict(
type='Pretrained', checkpoint=img_pretrained)
if self.with_img_roi_head:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
self.img_roi_head.init_cfg = dict(
type='Pretrained', checkpoint=img_pretrained)
if self.with_pts_backbone:
if img_pretrained is not None:
warnings.warn('DeprecationWarning: pretrained is a deprecated \
key, please consider using init_cfg')
self.pts_backbone.init_cfg = dict(
type='Pretrained', checkpoint=pts_pretrained)
@property
def with_img_shared_head(self):
"""bool: Whether the detector has a shared head in image branch."""
return hasattr(self,
'img_shared_head') and self.img_shared_head is not None
@property
def with_pts_bbox(self):
"""bool: Whether the detector has a 3D box head."""
return hasattr(self,
'pts_bbox_head') and self.pts_bbox_head is not None
@property
def with_img_bbox(self):
"""bool: Whether the detector has a 2D image box head."""
return hasattr(self,
'img_bbox_head') and self.img_bbox_head is not None
@property
def with_img_backbone(self):
"""bool: Whether the detector has a 2D image backbone."""
return hasattr(self, 'img_backbone') and self.img_backbone is not None
@property
def with_pts_backbone(self):
"""bool: Whether the detector has a 3D backbone."""
return hasattr(self, 'pts_backbone') and self.pts_backbone is not None
@property
def with_fusion(self):
"""bool: Whether the detector has a fusion layer."""
return hasattr(self,
'pts_fusion_layer') and self.fusion_layer is not None
@property
def with_img_neck(self):
"""bool: Whether the detector has a neck in image branch."""
return hasattr(self, 'img_neck') and self.img_neck is not None
@property
def with_pts_neck(self):
"""bool: Whether the detector has a neck in 3D detector branch."""
return hasattr(self, 'pts_neck') and self.pts_neck is not None
@property
def with_img_rpn(self):
"""bool: Whether the detector has a 2D RPN in image detector branch."""
return hasattr(self, 'img_rpn_head') and self.img_rpn_head is not None
@property
def with_img_roi_head(self):
"""bool: Whether the detector has a RoI Head in image branch."""
return hasattr(self, 'img_roi_head') and self.img_roi_head is not None
@property
def with_voxel_encoder(self):
"""bool: Whether the detector has a voxel encoder."""
return hasattr(self,
'voxel_encoder') and self.voxel_encoder is not None
@property
def with_middle_encoder(self):
"""bool: Whether the detector has a middle encoder."""
return hasattr(self,
'middle_encoder') and self.middle_encoder is not None
[docs] @torch.no_grad()
@force_fp32()
def voxelize(self, points):
"""Apply dynamic voxelization to points.
Args:
points (list[torch.Tensor]): Points of each sample.
Returns:
tuple[torch.Tensor]: Concatenated points, number of points
per voxel, and coordinates.
"""
voxels, coors, num_points = [], [], []
for res in points:
res_voxels, res_coors, res_num_points = self.pts_voxel_layer(res)
voxels.append(res_voxels)
coors.append(res_coors)
num_points.append(res_num_points)
voxels = torch.cat(voxels, dim=0)
num_points = torch.cat(num_points, dim=0)
coors_batch = []
for i, coor in enumerate(coors):
coor_pad = F.pad(coor, (1, 0), mode='constant', value=i)
coors_batch.append(coor_pad)
coors_batch = torch.cat(coors_batch, dim=0)
return voxels, num_points, coors_batch
[docs] def forward_train(self,
points=None,
img_metas=None,
gt_bboxes_3d=None,
gt_labels_3d=None,
gt_labels=None,
gt_bboxes=None,
img=None,
proposals=None,
gt_bboxes_ignore=None):
"""Forward training function.
Args:
points (list[torch.Tensor], optional): Points of each sample.
Defaults to None.
img_metas (list[dict], optional): Meta information of each sample.
Defaults to None.
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`], optional):
Ground truth 3D boxes. Defaults to None.
gt_labels_3d (list[torch.Tensor], optional): Ground truth labels
of 3D boxes. Defaults to None.
gt_labels (list[torch.Tensor], optional): Ground truth labels
of 2D boxes in images. Defaults to None.
gt_bboxes (list[torch.Tensor], optional): Ground truth 2D boxes in
images. Defaults to None.
img (torch.Tensor optional): Images of each sample with shape
(N, C, H, W). Defaults to None.
proposals ([list[torch.Tensor], optional): Predicted proposals
used for training Fast RCNN. Defaults to None.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
2D boxes in images to be ignored. Defaults to None.
Returns:
dict: Losses of different branches.
"""
img_feats, pts_feats = self.extract_feat(
points, img=img, img_metas=img_metas)
losses = dict()
if pts_feats:
losses_pts = self.forward_pts_train(pts_feats, gt_bboxes_3d,
gt_labels_3d, img_metas,
gt_bboxes_ignore)
losses.update(losses_pts)
if img_feats:
losses_img = self.forward_img_train(
img_feats,
img_metas=img_metas,
gt_bboxes=gt_bboxes,
gt_labels=gt_labels,
gt_bboxes_ignore=gt_bboxes_ignore,
proposals=proposals)
losses.update(losses_img)
return losses
[docs] def forward_pts_train(self,
pts_feats,
gt_bboxes_3d,
gt_labels_3d,
img_metas,
gt_bboxes_ignore=None):
"""Forward function for point cloud branch.
Args:
pts_feats (list[torch.Tensor]): Features of point cloud branch
gt_bboxes_3d (list[:obj:`BaseInstance3DBoxes`]): Ground truth
boxes for each sample.
gt_labels_3d (list[torch.Tensor]): Ground truth labels for
boxes of each sampole
img_metas (list[dict]): Meta information of samples.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
boxes to be ignored. Defaults to None.
Returns:
dict: Losses of each branch.
"""
outs = self.pts_bbox_head(pts_feats)
loss_inputs = outs + (gt_bboxes_3d, gt_labels_3d, img_metas)
losses = self.pts_bbox_head.loss(
*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
return losses
[docs] def forward_img_train(self,
x,
img_metas,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
proposals=None,
**kwargs):
"""Forward function for image branch.
This function works similar to the forward function of Faster R-CNN.
Args:
x (list[torch.Tensor]): Image features of shape (B, C, H, W)
of multiple levels.
img_metas (list[dict]): Meta information of images.
gt_bboxes (list[torch.Tensor]): Ground truth boxes of each image
sample.
gt_labels (list[torch.Tensor]): Ground truth labels of boxes.
gt_bboxes_ignore (list[torch.Tensor], optional): Ground truth
boxes to be ignored. Defaults to None.
proposals (list[torch.Tensor], optional): Proposals of each sample.
Defaults to None.
Returns:
dict: Losses of each branch.
"""
losses = dict()
# RPN forward and loss
if self.with_img_rpn:
rpn_outs = self.img_rpn_head(x)
rpn_loss_inputs = rpn_outs + (gt_bboxes, img_metas,
self.train_cfg.img_rpn)
rpn_losses = self.img_rpn_head.loss(
*rpn_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
losses.update(rpn_losses)
proposal_cfg = self.train_cfg.get('img_rpn_proposal',
self.test_cfg.img_rpn)
proposal_inputs = rpn_outs + (img_metas, proposal_cfg)
proposal_list = self.img_rpn_head.get_bboxes(*proposal_inputs)
else:
proposal_list = proposals
# bbox head forward and loss
if self.with_img_bbox:
# bbox head forward and loss
img_roi_losses = self.img_roi_head.forward_train(
x, img_metas, proposal_list, gt_bboxes, gt_labels,
gt_bboxes_ignore, **kwargs)
losses.update(img_roi_losses)
return losses
[docs] def simple_test_img(self, x, img_metas, proposals=None, rescale=False):
"""Test without augmentation."""
if proposals is None:
proposal_list = self.simple_test_rpn(x, img_metas,
self.test_cfg.img_rpn)
else:
proposal_list = proposals
return self.img_roi_head.simple_test(
x, proposal_list, img_metas, rescale=rescale)
[docs] def simple_test_rpn(self, x, img_metas, rpn_test_cfg):
"""RPN test function."""
rpn_outs = self.img_rpn_head(x)
proposal_inputs = rpn_outs + (img_metas, rpn_test_cfg)
proposal_list = self.img_rpn_head.get_bboxes(*proposal_inputs)
return proposal_list
[docs] def simple_test_pts(self, x, img_metas, rescale=False):
"""Test function of point cloud branch."""
outs = self.pts_bbox_head(x)
bbox_list = self.pts_bbox_head.get_bboxes(
*outs, img_metas, rescale=rescale)
bbox_results = [
bbox3d2result(bboxes, scores, labels)
for bboxes, scores, labels in bbox_list
]
return bbox_results
[docs] def simple_test(self, points, img_metas, img=None, rescale=False):
"""Test function without augmentaiton."""
img_feats, pts_feats = self.extract_feat(
points, img=img, img_metas=img_metas)
bbox_list = [dict() for i in range(len(img_metas))]
if pts_feats and self.with_pts_bbox:
bbox_pts = self.simple_test_pts(
pts_feats, img_metas, rescale=rescale)
for result_dict, pts_bbox in zip(bbox_list, bbox_pts):
result_dict['pts_bbox'] = pts_bbox
if img_feats and self.with_img_bbox:
bbox_img = self.simple_test_img(
img_feats, img_metas, rescale=rescale)
for result_dict, img_bbox in zip(bbox_list, bbox_img):
result_dict['img_bbox'] = img_bbox
return bbox_list
[docs] def aug_test(self, points, img_metas, imgs=None, rescale=False):
"""Test function with augmentaiton."""
img_feats, pts_feats = self.extract_feats(points, img_metas, imgs)
bbox_list = dict()
if pts_feats and self.with_pts_bbox:
bbox_pts = self.aug_test_pts(pts_feats, img_metas, rescale)
bbox_list.update(pts_bbox=bbox_pts)
return [bbox_list]
[docs] def aug_test_pts(self, feats, img_metas, rescale=False):
"""Test function of point cloud branch with augmentaiton."""
# only support aug_test for one sample
aug_bboxes = []
for x, img_meta in zip(feats, img_metas):
outs = self.pts_bbox_head(x)
bbox_list = self.pts_bbox_head.get_bboxes(
*outs, 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.pts_bbox_head.test_cfg)
return merged_bboxes
[docs] def show_results(self, data, result, out_dir):
"""Results visualization.
Args:
data (dict): Input points and the information of the sample.
result (dict): Prediction results.
out_dir (str): Output directory of visualization result.
"""
for batch_id in range(len(result)):
if isinstance(data['points'][0], DC):
points = data['points'][0]._data[0][batch_id].numpy()
elif mmcv.is_list_of(data['points'][0], torch.Tensor):
points = data['points'][0][batch_id]
else:
ValueError(f"Unsupported data type {type(data['points'][0])} "
f'for visualization!')
if isinstance(data['img_metas'][0], DC):
pts_filename = data['img_metas'][0]._data[0][batch_id][
'pts_filename']
box_mode_3d = data['img_metas'][0]._data[0][batch_id][
'box_mode_3d']
elif mmcv.is_list_of(data['img_metas'][0], dict):
pts_filename = data['img_metas'][0][batch_id]['pts_filename']
box_mode_3d = data['img_metas'][0][batch_id]['box_mode_3d']
else:
ValueError(
f"Unsupported data type {type(data['img_metas'][0])} "
f'for visualization!')
file_name = osp.split(pts_filename)[-1].split('.')[0]
assert out_dir is not None, 'Expect out_dir, got none.'
inds = result[batch_id]['pts_bbox']['scores_3d'] > 0.1
pred_bboxes = result[batch_id]['pts_bbox']['boxes_3d'][inds]
# for now we convert points and bbox into depth mode
if (box_mode_3d == Box3DMode.CAM) or (box_mode_3d
== Box3DMode.LIDAR):
points = Coord3DMode.convert_point(points, Coord3DMode.LIDAR,
Coord3DMode.DEPTH)
pred_bboxes = Box3DMode.convert(pred_bboxes, box_mode_3d,
Box3DMode.DEPTH)
elif box_mode_3d != Box3DMode.DEPTH:
ValueError(
f'Unsupported box_mode_3d {box_mode_3d} for convertion!')
pred_bboxes = pred_bboxes.tensor.cpu().numpy()
show_result(points, None, pred_bboxes, out_dir, file_name)