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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Sequence, Tuple
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmdet.models.roi_heads import CascadeRoIHead
from mmdet.models.task_modules.samplers import SamplingResult
from mmdet.models.test_time_augs import merge_aug_masks
from mmdet.models.utils.misc import empty_instances
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.structures.bbox import bbox2roi, get_box_tensor
from mmdet.utils import ConfigType, InstanceList, MultiConfig
@MODELS.register_module(force=True) # avoid bug
class DeticRoIHead(CascadeRoIHead):
def init_mask_head(self, mask_roi_extractor: MultiConfig,
mask_head: MultiConfig) -> None:
"""Initialize mask head and mask roi extractor.
Args:
mask_head (dict): Config of mask in mask head.
mask_roi_extractor (:obj:`ConfigDict`, dict or list):
Config of mask roi extractor.
"""
self.mask_head = MODELS.build(mask_head)
if mask_roi_extractor is not None:
self.share_roi_extractor = False
self.mask_roi_extractor = MODELS.build(mask_roi_extractor)
else:
self.share_roi_extractor = True
self.mask_roi_extractor = self.bbox_roi_extractor
def _refine_roi(self, x: Tuple[Tensor], rois: Tensor,
batch_img_metas: List[dict],
num_proposals_per_img: Sequence[int], **kwargs) -> tuple:
"""Multi-stage refinement of RoI.
Args:
x (tuple[Tensor]): List of multi-level img features.
rois (Tensor): shape (n, 5), [batch_ind, x1, y1, x2, y2]
batch_img_metas (list[dict]): List of image information.
num_proposals_per_img (sequence[int]): number of proposals
in each image.
Returns:
tuple:
- rois (Tensor): Refined RoI.
- cls_scores (list[Tensor]): Average predicted
cls score per image.
- bbox_preds (list[Tensor]): Bbox branch predictions
for the last stage of per image.
"""
# "ms" in variable names means multi-stage
ms_scores = []
for stage in range(self.num_stages):
bbox_results = self._bbox_forward(
stage=stage, x=x, rois=rois, **kwargs)
# split batch bbox prediction back to each image
cls_scores = bbox_results['cls_score'].sigmoid()
bbox_preds = bbox_results['bbox_pred']
rois = rois.split(num_proposals_per_img, 0)
cls_scores = cls_scores.split(num_proposals_per_img, 0)
ms_scores.append(cls_scores)
bbox_preds = bbox_preds.split(num_proposals_per_img, 0)
if stage < self.num_stages - 1:
bbox_head = self.bbox_head[stage]
refine_rois_list = []
for i in range(len(batch_img_metas)):
if rois[i].shape[0] > 0:
bbox_label = cls_scores[i][:, :-1].argmax(dim=1)
# Refactor `bbox_head.regress_by_class` to only accept
# box tensor without img_idx concatenated.
refined_bboxes = bbox_head.regress_by_class(
rois[i][:, 1:], bbox_label, bbox_preds[i],
batch_img_metas[i])
refined_bboxes = get_box_tensor(refined_bboxes)
refined_rois = torch.cat(
[rois[i][:, [0]], refined_bboxes], dim=1)
refine_rois_list.append(refined_rois)
rois = torch.cat(refine_rois_list)
# ms_scores aligned
# average scores of each image by stages
cls_scores = [
sum([score[i] for score in ms_scores]) / float(len(ms_scores))
for i in range(len(batch_img_metas))
] # aligned
return rois, cls_scores, bbox_preds
def _bbox_forward(self, stage: int, x: Tuple[Tensor],
rois: Tensor) -> 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.
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)
# do not support caffe_c4 model anymore
cls_score, bbox_pred = bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def predict_bbox(self,
x: Tuple[Tensor],
batch_img_metas: List[dict],
rpn_results_list: InstanceList,
rcnn_test_cfg: ConfigType,
rescale: bool = False,
**kwargs) -> InstanceList:
"""Perform forward propagation of the bbox head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
batch_img_metas (list[dict]): List of image information.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals.
rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
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).
"""
proposals = [res.bboxes for res in rpn_results_list]
proposal_scores = [res.scores for res in rpn_results_list]
num_proposals_per_img = tuple(len(p) for p in proposals)
rois = bbox2roi(proposals)
if rois.shape[0] == 0:
return empty_instances(
batch_img_metas,
rois.device,
task_type='bbox',
box_type=self.bbox_head[-1].predict_box_type,
num_classes=self.bbox_head[-1].num_classes,
score_per_cls=rcnn_test_cfg is None)
# rois aligned
rois, cls_scores, bbox_preds = self._refine_roi(
x=x,
rois=rois,
batch_img_metas=batch_img_metas,
num_proposals_per_img=num_proposals_per_img,
**kwargs)
# score reweighting in centernet2
cls_scores = [(s * ps[:, None])**0.5
for s, ps in zip(cls_scores, proposal_scores)]
cls_scores = [
s * (s == s[:, :-1].max(dim=1)[0][:, None]).float()
for s in cls_scores
]
# fast_rcnn_inference
results_list = self.bbox_head[-1].predict_by_feat(
rois=rois,
cls_scores=cls_scores,
bbox_preds=bbox_preds,
batch_img_metas=batch_img_metas,
rescale=rescale,
rcnn_test_cfg=rcnn_test_cfg)
return results_list
def _mask_forward(self, x: Tuple[Tensor], rois: Tensor) -> 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.
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)
# do not support caffe_c4 model anymore
mask_preds = self.mask_head(mask_feats)
mask_results = dict(mask_preds=mask_preds)
return mask_results
def mask_loss(self, x, sampling_results: List[SamplingResult],
batch_gt_instances: InstanceList) -> 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.
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)
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 loss(self, x: Tuple[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
"""
raise NotImplementedError
def predict_mask(self,
x: Tuple[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.
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,
mask_rois.device,
task_type='mask',
instance_results=results_list,
mask_thr_binary=self.test_cfg.mask_thr_binary)
return results_list
num_mask_rois_per_img = [len(res) for res in results_list]
aug_masks = []
mask_results = self._mask_forward(x, mask_rois)
mask_preds = mask_results['mask_preds']
# split batch mask prediction back to each image
mask_preds = mask_preds.split(num_mask_rois_per_img, 0)
aug_masks.append([m.sigmoid().detach() for m in mask_preds])
merged_masks = []
for i in range(len(batch_img_metas)):
aug_mask = [mask[i] for mask in aug_masks]
merged_mask = merge_aug_masks(aug_mask, batch_img_metas[i])
merged_masks.append(merged_mask)
results_list = self.mask_head.predict_by_feat(
mask_preds=merged_masks,
results_list=results_list,
batch_img_metas=batch_img_metas,
rcnn_test_cfg=self.test_cfg,
rescale=rescale,
activate_map=True)
return results_list
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