from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
[docs]class BaseMono3DDenseHead(BaseModule, metaclass=ABCMeta):
"""Base class for Monocular 3D DenseHeads."""
def __init__(self, init_cfg=None):
super(BaseMono3DDenseHead, self).__init__(init_cfg=init_cfg)
[docs] @abstractmethod
def loss(self, **kwargs):
"""Compute losses of the head."""
pass
[docs] @abstractmethod
def get_bboxes(self, **kwargs):
"""Transform network output for a batch into bbox predictions."""
pass
[docs] def forward_train(self,
x,
img_metas,
gt_bboxes,
gt_labels=None,
gt_bboxes_3d=None,
gt_labels_3d=None,
centers2d=None,
depths=None,
attr_labels=None,
gt_bboxes_ignore=None,
proposal_cfg=None,
**kwargs):
"""
Args:
x (list[Tensor]): Features from FPN.
img_metas (list[dict]): Meta information of each image, e.g.,
image size, scaling factor, etc.
gt_bboxes (list[Tensor]): Ground truth bboxes of the image,
shape (num_gts, 4).
gt_labels (list[Tensor]): Ground truth labels of each box,
shape (num_gts,).
gt_bboxes_3d (list[Tensor]): 3D ground truth bboxes of the image,
shape (num_gts, self.bbox_code_size).
gt_labels_3d (list[Tensor]): 3D ground truth labels of each box,
shape (num_gts,).
centers2d (list[Tensor]): Projected 3D center of each box,
shape (num_gts, 2).
depths (list[Tensor]): Depth of projected 3D center of each box,
shape (num_gts,).
attr_labels (list[Tensor]): Attribute labels of each box,
shape (num_gts,).
gt_bboxes_ignore (list[Tensor]): Ground truth bboxes to be
ignored, shape (num_ignored_gts, 4).
proposal_cfg (mmcv.Config): Test / postprocessing configuration,
if None, test_cfg would be used
Returns:
tuple:
losses: (dict[str, Tensor]): A dictionary of loss components.
proposal_list (list[Tensor]): Proposals of each image.
"""
outs = self(x)
if gt_labels is None:
loss_inputs = outs + (gt_bboxes, gt_bboxes_3d, centers2d, depths,
attr_labels, img_metas)
else:
loss_inputs = outs + (gt_bboxes, gt_labels, gt_bboxes_3d,
gt_labels_3d, centers2d, depths, attr_labels,
img_metas)
losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
if proposal_cfg is None:
return losses
else:
proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
return losses, proposal_list