Source code for mmdet3d.models.detectors.imvoxelnet
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet3d.core import bbox3d2result, build_prior_generator
from mmdet3d.core.bbox.structures.utils import get_proj_mat_by_coord_type
from mmdet3d.models.fusion_layers.point_fusion import point_sample
from mmdet.models.detectors import BaseDetector
from ..builder import DETECTORS, build_backbone, build_head, build_neck
[docs]@DETECTORS.register_module()
class ImVoxelNet(BaseDetector):
r"""`ImVoxelNet <https://arxiv.org/abs/2106.01178>`_.
Args:
backbone (dict): Config of the backbone.
neck (dict): Config of the 2d neck.
neck_3d (dict): Config of the 3d neck.
bbox_head (dict): Config of the head.
prior_generator (dict): Config of the prior generator.
n_voxels (tuple[int]): Number of voxels for x, y, and z axis.
coord_type (str): The type of coordinates of points cloud:
'DEPTH', 'LIDAR', or 'CAMERA'.
train_cfg (dict, optional): Config for train stage. Defaults to None.
test_cfg (dict, optional): Config for test stage. Defaults to None.
init_cfg (dict, optional): Config for weight initialization.
Defaults to None.
pretrained (str, optional): Deprecated initialization parameter.
Defaults to None.
"""
def __init__(self,
backbone,
neck,
neck_3d,
bbox_head,
prior_generator,
n_voxels,
coord_type,
train_cfg=None,
test_cfg=None,
init_cfg=None,
pretrained=None):
super().__init__(init_cfg=init_cfg)
self.backbone = build_backbone(backbone)
self.neck = build_neck(neck)
self.neck_3d = build_neck(neck_3d)
bbox_head.update(train_cfg=train_cfg)
bbox_head.update(test_cfg=test_cfg)
self.bbox_head = build_head(bbox_head)
self.n_voxels = n_voxels
self.coord_type = coord_type
self.prior_generator = build_prior_generator(prior_generator)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
[docs] def extract_feat(self, img, img_metas):
"""Extract 3d features from the backbone -> fpn -> 3d projection.
-> 3d neck -> bbox_head.
Args:
img (torch.Tensor): Input images of shape (N, C_in, H, W).
img_metas (list): Image metas.
Returns:
Tuple:
- torch.Tensor: Features of shape (N, C_out, N_x, N_y, N_z).
- torch.Tensor: Valid mask of shape (N, 1, N_x, N_y, N_z).
"""
x = self.backbone(img)
x = self.neck(x)[0]
points = self.prior_generator.grid_anchors([self.n_voxels[::-1]],
device=img.device)[0][:, :3]
volumes, valid_preds = [], []
for feature, img_meta in zip(x, img_metas):
img_scale_factor = (
points.new_tensor(img_meta['scale_factor'][:2])
if 'scale_factor' in img_meta.keys() else 1)
img_flip = img_meta['flip'] if 'flip' in img_meta.keys() else False
img_crop_offset = (
points.new_tensor(img_meta['img_crop_offset'])
if 'img_crop_offset' in img_meta.keys() else 0)
proj_mat = points.new_tensor(
get_proj_mat_by_coord_type(img_meta, self.coord_type))
volume = point_sample(
img_meta,
img_features=feature[None, ...],
points=points,
proj_mat=points.new_tensor(proj_mat),
coord_type=self.coord_type,
img_scale_factor=img_scale_factor,
img_crop_offset=img_crop_offset,
img_flip=img_flip,
img_pad_shape=img.shape[-2:],
img_shape=img_meta['img_shape'][:2],
aligned=False)
volumes.append(
volume.reshape(self.n_voxels[::-1] + [-1]).permute(3, 2, 1, 0))
valid_preds.append(
~torch.all(volumes[-1] == 0, dim=0, keepdim=True))
x = torch.stack(volumes)
x = self.neck_3d(x)
x = self.bbox_head(x)
return x, torch.stack(valid_preds).float()
[docs] def forward_train(self, img, img_metas, gt_bboxes_3d, gt_labels_3d,
**kwargs):
"""Forward of training.
Args:
img (torch.Tensor): Input images of shape (N, C_in, H, W).
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.
Returns:
dict[str, torch.Tensor]: A dictionary of loss components.
"""
x, valid_preds = self.extract_feat(img, img_metas)
# For indoor datasets ImVoxelNet uses ImVoxelHead that handles
# mask of visible voxels.
if self.coord_type == 'DEPTH':
x += (valid_preds, )
losses = self.bbox_head.loss(*x, gt_bboxes_3d, gt_labels_3d, img_metas)
return losses
[docs] def forward_test(self, img, img_metas, **kwargs):
"""Forward of testing.
Args:
img (torch.Tensor): Input images of shape (N, C_in, H, W).
img_metas (list): Image metas.
Returns:
list[dict]: Predicted 3d boxes.
"""
# not supporting aug_test for now
return self.simple_test(img, img_metas)
[docs] def simple_test(self, img, img_metas):
"""Test without augmentations.
Args:
img (torch.Tensor): Input images of shape (N, C_in, H, W).
img_metas (list): Image metas.
Returns:
list[dict]: Predicted 3d boxes.
"""
x, valid_preds = self.extract_feat(img, img_metas)
# For indoor datasets ImVoxelNet uses ImVoxelHead that handles
# mask of visible voxels.
if self.coord_type == 'DEPTH':
x += (valid_preds, )
bbox_list = self.bbox_head.get_bboxes(*x, img_metas)
bbox_results = [
bbox3d2result(det_bboxes, det_scores, det_labels)
for det_bboxes, det_scores, det_labels in bbox_list
]
return bbox_results
[docs] def aug_test(self, imgs, img_metas, **kwargs):
"""Test with augmentations.
Args:
imgs (list[torch.Tensor]): Input images of shape (N, C_in, H, W).
img_metas (list): Image metas.
Returns:
list[dict]: Predicted 3d boxes.
"""
raise NotImplementedError