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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import bbox2roi
from mmdet.utils import ConfigType, InstanceList, OptConfigType
from ..layers import adaptive_avg_pool2d
from ..task_modules.samplers import SamplingResult
from ..utils import empty_instances, unpack_gt_instances
from .cascade_roi_head import CascadeRoIHead
@MODELS.register_module()
class SCNetRoIHead(CascadeRoIHead):
"""RoIHead for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
num_stages (int): number of cascade stages.
stage_loss_weights (list): loss weight of cascade stages.
semantic_roi_extractor (dict): config to init semantic roi extractor.
semantic_head (dict): config to init semantic head.
feat_relay_head (dict): config to init feature_relay_head.
glbctx_head (dict): config to init global context head.
"""
def __init__(self,
num_stages: int,
stage_loss_weights: List[float],
semantic_roi_extractor: OptConfigType = None,
semantic_head: OptConfigType = None,
feat_relay_head: OptConfigType = None,
glbctx_head: OptConfigType = None,
**kwargs) -> None:
super().__init__(
num_stages=num_stages,
stage_loss_weights=stage_loss_weights,
**kwargs)
assert self.with_bbox and self.with_mask
assert not self.with_shared_head # shared head is not supported
if semantic_head is not None:
self.semantic_roi_extractor = MODELS.build(semantic_roi_extractor)
self.semantic_head = MODELS.build(semantic_head)
if feat_relay_head is not None:
self.feat_relay_head = MODELS.build(feat_relay_head)
if glbctx_head is not None:
self.glbctx_head = MODELS.build(glbctx_head)
def init_mask_head(self, mask_roi_extractor: ConfigType,
mask_head: ConfigType) -> None:
"""Initialize ``mask_head``"""
if mask_roi_extractor is not None:
self.mask_roi_extractor = MODELS.build(mask_roi_extractor)
self.mask_head = MODELS.build(mask_head)
# TODO move to base_roi_head later
@property
def with_semantic(self) -> bool:
"""bool: whether the head has semantic head"""
return hasattr(self,
'semantic_head') and self.semantic_head is not None
@property
def with_feat_relay(self) -> bool:
"""bool: whether the head has feature relay head"""
return (hasattr(self, 'feat_relay_head')
and self.feat_relay_head is not None)
@property
def with_glbctx(self) -> bool:
"""bool: whether the head has global context head"""
return hasattr(self, 'glbctx_head') and self.glbctx_head is not None
def _fuse_glbctx(self, roi_feats: Tensor, glbctx_feat: Tensor,
rois: Tensor) -> Tensor:
"""Fuse global context feats with roi feats.
Args:
roi_feats (Tensor): RoI features.
glbctx_feat (Tensor): Global context feature..
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
Returns:
Tensor: Fused feature.
"""
assert roi_feats.size(0) == rois.size(0)
# RuntimeError: isDifferentiableType(variable.scalar_type())
# INTERNAL ASSERT FAILED if detach() is not used when calling
# roi_head.predict().
img_inds = torch.unique(rois[:, 0].detach().cpu(), sorted=True).long()
fused_feats = torch.zeros_like(roi_feats)
for img_id in img_inds:
inds = (rois[:, 0] == img_id.item())
fused_feats[inds] = roi_feats[inds] + glbctx_feat[img_id]
return fused_feats
def _slice_pos_feats(self, feats: Tensor,
sampling_results: List[SamplingResult]) -> Tensor:
"""Get features from pos rois.
Args:
feats (Tensor): Input features.
sampling_results (list["obj:`SamplingResult`]): Sampling results.
Returns:
Tensor: Sliced features.
"""
num_rois = [res.priors.size(0) for res in sampling_results]
num_pos_rois = [res.pos_priors.size(0) for res in sampling_results]
inds = torch.zeros(sum(num_rois), dtype=torch.bool)
start = 0
for i in range(len(num_rois)):
start = 0 if i == 0 else start + num_rois[i - 1]
stop = start + num_pos_rois[i]
inds[start:stop] = 1
sliced_feats = feats[inds]
return sliced_feats
def _bbox_forward(self,
stage: int,
x: Tuple[Tensor],
rois: Tensor,
semantic_feat: Optional[Tensor] = None,
glbctx_feat: Optional[Tensor] = None) -> dict:
"""Box head forward function used in both training and testing.
Args:
stage (int): The current stage in Cascade RoI Head.
x (tuple[Tensor]): List of multi-level img features.
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
semantic_feat (Tensor): Semantic feature. Defaults to None.
glbctx_feat (Tensor): Global context feature. Defaults to None.
Returns:
dict[str, Tensor]: Usually returns a dictionary with keys:
- `cls_score` (Tensor): Classification scores.
- `bbox_pred` (Tensor): Box energies / deltas.
- `bbox_feats` (Tensor): Extract bbox RoI features.
"""
bbox_roi_extractor = self.bbox_roi_extractor[stage]
bbox_head = self.bbox_head[stage]
bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
rois)
if self.with_semantic and semantic_feat is not None:
bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat],
rois)
if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]:
bbox_semantic_feat = adaptive_avg_pool2d(
bbox_semantic_feat, bbox_feats.shape[-2:])
bbox_feats += bbox_semantic_feat
if self.with_glbctx and glbctx_feat is not None:
bbox_feats = self._fuse_glbctx(bbox_feats, glbctx_feat, rois)
cls_score, bbox_pred, relayed_feat = bbox_head(
bbox_feats, return_shared_feat=True)
bbox_results = dict(
cls_score=cls_score,
bbox_pred=bbox_pred,
relayed_feat=relayed_feat)
return bbox_results
def _mask_forward(self,
x: Tuple[Tensor],
rois: Tensor,
semantic_feat: Optional[Tensor] = None,
glbctx_feat: Optional[Tensor] = None,
relayed_feat: Optional[Tensor] = None) -> dict:
"""Mask head forward function used in both training and testing.
Args:
stage (int): The current stage in Cascade RoI Head.
x (tuple[Tensor]): Tuple of multi-level img features.
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
semantic_feat (Tensor): Semantic feature. Defaults to None.
glbctx_feat (Tensor): Global context feature. Defaults to None.
relayed_feat (Tensor): Relayed feature. Defaults to None.
Returns:
dict: Usually returns a dictionary with keys:
- `mask_preds` (Tensor): Mask prediction.
"""
mask_feats = self.mask_roi_extractor(
x[:self.mask_roi_extractor.num_inputs], rois)
if self.with_semantic and semantic_feat is not None:
mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
rois)
if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
mask_semantic_feat = F.adaptive_avg_pool2d(
mask_semantic_feat, mask_feats.shape[-2:])
mask_feats += mask_semantic_feat
if self.with_glbctx and glbctx_feat is not None:
mask_feats = self._fuse_glbctx(mask_feats, glbctx_feat, rois)
if self.with_feat_relay and relayed_feat is not None:
mask_feats = mask_feats + relayed_feat
mask_preds = self.mask_head(mask_feats)
mask_results = dict(mask_preds=mask_preds)
return mask_results
def bbox_loss(self,
stage: int,
x: Tuple[Tensor],
sampling_results: List[SamplingResult],
semantic_feat: Optional[Tensor] = None,
glbctx_feat: Optional[Tensor] = None) -> dict:
"""Run forward function and calculate loss for box head in training.
Args:
stage (int): The current stage in Cascade RoI Head.
x (tuple[Tensor]): List of multi-level img features.
sampling_results (list["obj:`SamplingResult`]): Sampling results.
semantic_feat (Tensor): Semantic feature. Defaults to None.
glbctx_feat (Tensor): Global context feature. Defaults to None.
Returns:
dict: Usually returns a dictionary with keys:
- `cls_score` (Tensor): Classification scores.
- `bbox_pred` (Tensor): Box energies / deltas.
- `bbox_feats` (Tensor): Extract bbox RoI features.
- `loss_bbox` (dict): A dictionary of bbox loss components.
- `rois` (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
- `bbox_targets` (tuple): Ground truth for proposals in a
single image. Containing the following list of Tensors:
(labels, label_weights, bbox_targets, bbox_weights)
"""
bbox_head = self.bbox_head[stage]
rois = bbox2roi([res.priors for res in sampling_results])
bbox_results = self._bbox_forward(
stage,
x,
rois,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat)
bbox_results.update(rois=rois)
bbox_loss_and_target = bbox_head.loss_and_target(
cls_score=bbox_results['cls_score'],
bbox_pred=bbox_results['bbox_pred'],
rois=rois,
sampling_results=sampling_results,
rcnn_train_cfg=self.train_cfg[stage])
bbox_results.update(bbox_loss_and_target)
return bbox_results
def mask_loss(self,
x: Tuple[Tensor],
sampling_results: List[SamplingResult],
batch_gt_instances: InstanceList,
semantic_feat: Optional[Tensor] = None,
glbctx_feat: Optional[Tensor] = None,
relayed_feat: Optional[Tensor] = None) -> dict:
"""Run forward function and calculate loss for mask head in training.
Args:
x (tuple[Tensor]): Tuple of multi-level img features.
sampling_results (list["obj:`SamplingResult`]): Sampling results.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes``, ``labels``, and
``masks`` attributes.
semantic_feat (Tensor): Semantic feature. Defaults to None.
glbctx_feat (Tensor): Global context feature. Defaults to None.
relayed_feat (Tensor): Relayed feature. Defaults to None.
Returns:
dict: Usually returns a dictionary with keys:
- `mask_preds` (Tensor): Mask prediction.
- `loss_mask` (dict): A dictionary of mask loss components.
"""
pos_rois = bbox2roi([res.pos_priors for res in sampling_results])
mask_results = self._mask_forward(
x,
pos_rois,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat,
relayed_feat=relayed_feat)
mask_loss_and_target = self.mask_head.loss_and_target(
mask_preds=mask_results['mask_preds'],
sampling_results=sampling_results,
batch_gt_instances=batch_gt_instances,
rcnn_train_cfg=self.train_cfg[-1])
mask_results.update(mask_loss_and_target)
return mask_results
def semantic_loss(self, x: Tuple[Tensor],
batch_data_samples: SampleList) -> dict:
"""Semantic segmentation loss.
Args:
x (Tuple[Tensor]): Tuple of multi-level img features.
batch_data_samples (list[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Returns:
dict: Usually returns a dictionary with keys:
- `semantic_feat` (Tensor): Semantic feature.
- `loss_seg` (dict): Semantic segmentation loss.
"""
gt_semantic_segs = [
data_sample.gt_sem_seg.sem_seg
for data_sample in batch_data_samples
]
gt_semantic_segs = torch.stack(gt_semantic_segs)
semantic_pred, semantic_feat = self.semantic_head(x)
loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_segs)
semantic_results = dict(loss_seg=loss_seg, semantic_feat=semantic_feat)
return semantic_results
def global_context_loss(self, x: Tuple[Tensor],
batch_gt_instances: InstanceList) -> dict:
"""Global context loss.
Args:
x (Tuple[Tensor]): Tuple of multi-level img features.
batch_gt_instances (list[:obj:`InstanceData`]): Batch of
gt_instance. It usually includes ``bboxes``, ``labels``, and
``masks`` attributes.
Returns:
dict: Usually returns a dictionary with keys:
- `glbctx_feat` (Tensor): Global context feature.
- `loss_glbctx` (dict): Global context loss.
"""
gt_labels = [
gt_instances.labels for gt_instances in batch_gt_instances
]
mc_pred, glbctx_feat = self.glbctx_head(x)
loss_glbctx = self.glbctx_head.loss(mc_pred, gt_labels)
global_context_results = dict(
loss_glbctx=loss_glbctx, glbctx_feat=glbctx_feat)
return global_context_results
def loss(self, x: Tensor, rpn_results_list: InstanceList,
batch_data_samples: SampleList) -> dict:
"""Perform forward propagation and loss calculation of the detection
roi on the features of the upstream network.
Args:
x (tuple[Tensor]): List of multi-level img features.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
batch_data_samples (list[:obj:`DetDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
Returns:
dict[str, Tensor]: A dictionary of loss components
"""
assert len(rpn_results_list) == len(batch_data_samples)
outputs = unpack_gt_instances(batch_data_samples)
batch_gt_instances, batch_gt_instances_ignore, batch_img_metas \
= outputs
losses = dict()
# semantic segmentation branch
if self.with_semantic:
semantic_results = self.semantic_loss(
x=x, batch_data_samples=batch_data_samples)
losses['loss_semantic_seg'] = semantic_results['loss_seg']
semantic_feat = semantic_results['semantic_feat']
else:
semantic_feat = None
# global context branch
if self.with_glbctx:
global_context_results = self.global_context_loss(
x=x, batch_gt_instances=batch_gt_instances)
losses['loss_glbctx'] = global_context_results['loss_glbctx']
glbctx_feat = global_context_results['glbctx_feat']
else:
glbctx_feat = None
results_list = rpn_results_list
num_imgs = len(batch_img_metas)
for stage in range(self.num_stages):
stage_loss_weight = self.stage_loss_weights[stage]
# assign gts and sample proposals
sampling_results = []
bbox_assigner = self.bbox_assigner[stage]
bbox_sampler = self.bbox_sampler[stage]
for i in range(num_imgs):
results = results_list[i]
# rename rpn_results.bboxes to rpn_results.priors
results.priors = results.pop('bboxes')
assign_result = bbox_assigner.assign(
results, batch_gt_instances[i],
batch_gt_instances_ignore[i])
sampling_result = bbox_sampler.sample(
assign_result,
results,
batch_gt_instances[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
sampling_results.append(sampling_result)
# bbox head forward and loss
bbox_results = self.bbox_loss(
stage=stage,
x=x,
sampling_results=sampling_results,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat)
for name, value in bbox_results['loss_bbox'].items():
losses[f's{stage}.{name}'] = (
value * stage_loss_weight if 'loss' in name else value)
# refine bboxes
if stage < self.num_stages - 1:
bbox_head = self.bbox_head[stage]
with torch.no_grad():
results_list = bbox_head.refine_bboxes(
sampling_results=sampling_results,
bbox_results=bbox_results,
batch_img_metas=batch_img_metas)
if self.with_feat_relay:
relayed_feat = self._slice_pos_feats(bbox_results['relayed_feat'],
sampling_results)
relayed_feat = self.feat_relay_head(relayed_feat)
else:
relayed_feat = None
# mask head forward and loss
mask_results = self.mask_loss(
x=x,
sampling_results=sampling_results,
batch_gt_instances=batch_gt_instances,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat,
relayed_feat=relayed_feat)
mask_stage_loss_weight = sum(self.stage_loss_weights)
losses['loss_mask'] = mask_stage_loss_weight * mask_results[
'loss_mask']['loss_mask']
return losses
def predict(self,
x: Tuple[Tensor],
rpn_results_list: InstanceList,
batch_data_samples: SampleList,
rescale: bool = False) -> InstanceList:
"""Perform forward propagation of the roi head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Features from upstream network. Each
has shape (N, C, H, W).
rpn_results_list (list[:obj:`InstanceData`]): list of region
proposals.
batch_data_samples (List[:obj:`DetDataSample`]): The Data
Samples. It usually includes information such as
`gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
rescale (bool): Whether to rescale the results to
the original image. Defaults to False.
Returns:
list[obj:`InstanceData`]: Detection results of each image.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
assert self.with_bbox, 'Bbox head must be implemented.'
batch_img_metas = [
data_samples.metainfo for data_samples in batch_data_samples
]
if self.with_semantic:
_, semantic_feat = self.semantic_head(x)
else:
semantic_feat = None
if self.with_glbctx:
_, glbctx_feat = self.glbctx_head(x)
else:
glbctx_feat = None
# TODO: nms_op in mmcv need be enhanced, the bbox result may get
# difference when not rescale in bbox_head
# If it has the mask branch, the bbox branch does not need
# to be scaled to the original image scale, because the mask
# branch will scale both bbox and mask at the same time.
bbox_rescale = rescale if not self.with_mask else False
results_list = self.predict_bbox(
x=x,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat,
batch_img_metas=batch_img_metas,
rpn_results_list=rpn_results_list,
rcnn_test_cfg=self.test_cfg,
rescale=bbox_rescale)
if self.with_mask:
results_list = self.predict_mask(
x=x,
semantic_heat=semantic_feat,
glbctx_feat=glbctx_feat,
batch_img_metas=batch_img_metas,
results_list=results_list,
rescale=rescale)
return results_list
def predict_mask(self,
x: Tuple[Tensor],
semantic_heat: Tensor,
glbctx_feat: Tensor,
batch_img_metas: List[dict],
results_list: List[InstanceData],
rescale: bool = False) -> List[InstanceData]:
"""Perform forward propagation of the mask head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
semantic_feat (Tensor): Semantic feature.
glbctx_feat (Tensor): Global context feature.
batch_img_metas (list[dict]): List of image information.
results_list (list[:obj:`InstanceData`]): Detection results of
each image.
rescale (bool): If True, return boxes in original image space.
Defaults to False.
Returns:
list[:obj:`InstanceData`]: Detection results of each image
after the post process.
Each item usually contains following keys.
- scores (Tensor): Classification scores, has a shape
(num_instance, )
- labels (Tensor): Labels of bboxes, has a shape
(num_instances, ).
- bboxes (Tensor): Has a shape (num_instances, 4),
the last dimension 4 arrange as (x1, y1, x2, y2).
- masks (Tensor): Has a shape (num_instances, H, W).
"""
bboxes = [res.bboxes for res in results_list]
mask_rois = bbox2roi(bboxes)
if mask_rois.shape[0] == 0:
results_list = empty_instances(
batch_img_metas=batch_img_metas,
device=mask_rois.device,
task_type='mask',
instance_results=results_list,
mask_thr_binary=self.test_cfg.mask_thr_binary)
return results_list
bboxes_results = self._bbox_forward(
stage=-1,
x=x,
rois=mask_rois,
semantic_feat=semantic_heat,
glbctx_feat=glbctx_feat)
relayed_feat = bboxes_results['relayed_feat']
relayed_feat = self.feat_relay_head(relayed_feat)
mask_results = self._mask_forward(
x=x,
rois=mask_rois,
semantic_feat=semantic_heat,
glbctx_feat=glbctx_feat,
relayed_feat=relayed_feat)
mask_preds = mask_results['mask_preds']
# split batch mask prediction back to each image
num_bbox_per_img = tuple(len(_bbox) for _bbox in bboxes)
mask_preds = mask_preds.split(num_bbox_per_img, 0)
results_list = self.mask_head.predict_by_feat(
mask_preds=mask_preds,
results_list=results_list,
batch_img_metas=batch_img_metas,
rcnn_test_cfg=self.test_cfg,
rescale=rescale)
return results_list
def forward(self, x: Tuple[Tensor], rpn_results_list: InstanceList,
batch_data_samples: SampleList) -> tuple:
"""Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
x (List[Tensor]): Multi-level features that may have different
resolutions.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
the meta information of each image and corresponding
annotations.
Returns
tuple: A tuple of features from ``bbox_head`` and ``mask_head``
forward.
"""
results = ()
batch_img_metas = [
data_samples.metainfo for data_samples in batch_data_samples
]
if self.with_semantic:
_, semantic_feat = self.semantic_head(x)
else:
semantic_feat = None
if self.with_glbctx:
_, glbctx_feat = self.glbctx_head(x)
else:
glbctx_feat = None
proposals = [rpn_results.bboxes for rpn_results in rpn_results_list]
num_proposals_per_img = tuple(len(p) for p in proposals)
rois = bbox2roi(proposals)
# bbox head
if self.with_bbox:
rois, cls_scores, bbox_preds = self._refine_roi(
x=x,
rois=rois,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat,
batch_img_metas=batch_img_metas,
num_proposals_per_img=num_proposals_per_img)
results = results + (cls_scores, bbox_preds)
# mask head
if self.with_mask:
rois = torch.cat(rois)
bboxes_results = self._bbox_forward(
stage=-1,
x=x,
rois=rois,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat)
relayed_feat = bboxes_results['relayed_feat']
relayed_feat = self.feat_relay_head(relayed_feat)
mask_results = self._mask_forward(
x=x,
rois=rois,
semantic_feat=semantic_feat,
glbctx_feat=glbctx_feat,
relayed_feat=relayed_feat)
mask_preds = mask_results['mask_preds']
mask_preds = mask_preds.split(num_proposals_per_img, 0)
results = results + (mask_preds, )
return results
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