from mmdet.core import bbox2roi
from ..builder import HEADS
from ..losses.pisa_loss import carl_loss, isr_p
from .standard_roi_head import StandardRoIHead
[docs]@HEADS.register_module()
class PISARoIHead(StandardRoIHead):
r"""The RoI head for `Prime Sample Attention in Object Detection
<https://arxiv.org/abs/1904.04821>`_."""
[docs] def forward_train(self,
x,
img_metas,
proposal_list,
gt_bboxes,
gt_labels,
gt_bboxes_ignore=None,
gt_masks=None):
"""Forward function for training.
Args:
x (list[Tensor]): List of multi-level img features.
img_metas (list[dict]): List of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
proposals (list[Tensors]): List of region proposals.
gt_bboxes (list[Tensor]): Each item are the truth boxes for each
image in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): Class indices corresponding to each box
gt_bboxes_ignore (list[Tensor], optional): Specify which bounding
boxes can be ignored when computing the loss.
gt_masks (None | Tensor) : True segmentation masks for each box
used if the architecture supports a segmentation task.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# assign gts and sample proposals
if self.with_bbox or self.with_mask:
num_imgs = len(img_metas)
if gt_bboxes_ignore is None:
gt_bboxes_ignore = [None for _ in range(num_imgs)]
sampling_results = []
neg_label_weights = []
for i in range(num_imgs):
assign_result = self.bbox_assigner.assign(
proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
gt_labels[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
proposal_list[i],
gt_bboxes[i],
gt_labels[i],
feats=[lvl_feat[i][None] for lvl_feat in x])
# neg label weight is obtained by sampling when using ISR-N
neg_label_weight = None
if isinstance(sampling_result, tuple):
sampling_result, neg_label_weight = sampling_result
sampling_results.append(sampling_result)
neg_label_weights.append(neg_label_weight)
losses = dict()
# bbox head forward and loss
if self.with_bbox:
bbox_results = self._bbox_forward_train(
x,
sampling_results,
gt_bboxes,
gt_labels,
img_metas,
neg_label_weights=neg_label_weights)
losses.update(bbox_results['loss_bbox'])
# mask head forward and loss
if self.with_mask:
mask_results = self._mask_forward_train(x, sampling_results,
bbox_results['bbox_feats'],
gt_masks, img_metas)
losses.update(mask_results['loss_mask'])
return losses
def _bbox_forward(self, x, rois):
"""Box forward function used in both training and testing."""
# TODO: a more flexible way to decide which feature maps to use
bbox_feats = self.bbox_roi_extractor(
x[:self.bbox_roi_extractor.num_inputs], rois)
if self.with_shared_head:
bbox_feats = self.shared_head(bbox_feats)
cls_score, bbox_pred = self.bbox_head(bbox_feats)
bbox_results = dict(
cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
return bbox_results
def _bbox_forward_train(self,
x,
sampling_results,
gt_bboxes,
gt_labels,
img_metas,
neg_label_weights=None):
"""Run forward function and calculate loss for box head in training."""
rois = bbox2roi([res.bboxes for res in sampling_results])
bbox_results = self._bbox_forward(x, rois)
bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
gt_labels, self.train_cfg)
# neg_label_weights obtained by sampler is image-wise, mapping back to
# the corresponding location in label weights
if neg_label_weights[0] is not None:
label_weights = bbox_targets[1]
cur_num_rois = 0
for i in range(len(sampling_results)):
num_pos = sampling_results[i].pos_inds.size(0)
num_neg = sampling_results[i].neg_inds.size(0)
label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos +
num_neg] = neg_label_weights[i]
cur_num_rois += num_pos + num_neg
cls_score = bbox_results['cls_score']
bbox_pred = bbox_results['bbox_pred']
# Apply ISR-P
isr_cfg = self.train_cfg.get('isr', None)
if isr_cfg is not None:
bbox_targets = isr_p(
cls_score,
bbox_pred,
bbox_targets,
rois,
sampling_results,
self.bbox_head.loss_cls,
self.bbox_head.bbox_coder,
**isr_cfg,
num_class=self.bbox_head.num_classes)
loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois,
*bbox_targets)
# Add CARL Loss
carl_cfg = self.train_cfg.get('carl', None)
if carl_cfg is not None:
loss_carl = carl_loss(
cls_score,
bbox_targets[0],
bbox_pred,
bbox_targets[2],
self.bbox_head.loss_bbox,
**carl_cfg,
num_class=self.bbox_head.num_classes)
loss_bbox.update(loss_carl)
bbox_results.update(loss_bbox=loss_bbox)
return bbox_results