from abc import ABCMeta, abstractmethod
import torch.nn as nn
from ..builder import build_shared_head
[docs]class BaseRoIHead(nn.Module, metaclass=ABCMeta):
"""Base class for RoIHeads"""
def __init__(self,
bbox_roi_extractor=None,
bbox_head=None,
mask_roi_extractor=None,
mask_head=None,
shared_head=None,
train_cfg=None,
test_cfg=None):
super(BaseRoIHead, self).__init__()
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if shared_head is not None:
self.shared_head = build_shared_head(shared_head)
if bbox_head is not None:
self.init_bbox_head(bbox_roi_extractor, bbox_head)
if mask_head is not None:
self.init_mask_head(mask_roi_extractor, mask_head)
self.init_assigner_sampler()
@property
def with_bbox(self):
return hasattr(self, 'bbox_head') and self.bbox_head is not None
@property
def with_mask(self):
return hasattr(self, 'mask_head') and self.mask_head is not None
@property
def with_shared_head(self):
return hasattr(self, 'shared_head') and self.shared_head is not None
@abstractmethod
def init_weights(self, pretrained):
pass
@abstractmethod
def init_bbox_head(self):
pass
@abstractmethod
def init_mask_head(self):
pass
@abstractmethod
def init_assigner_sampler(self):
pass
[docs] @abstractmethod
def forward_train(self,
x,
img_meta,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None,
**kwargs):
"""Forward function during training"""
pass
async def async_simple_test(self, x, img_meta, **kwargs):
raise NotImplementedError
[docs] def simple_test(self,
x,
proposal_list,
img_meta,
proposals=None,
rescale=False,
**kwargs):
"""Test without augmentation."""
pass
[docs] def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs):
"""Test with augmentations.
If rescale is False, then returned bboxes and masks will fit the scale
of imgs[0].
"""
pass