def _get_proposal_pairs(self, proposals):
proposal_pairs = []
for i, proposals_per_image in enumerate(proposals):
box_subj = proposals_per_image.bbox
box_obj = proposals_per_image.bbox
box_subj = box_subj.unsqueeze(1).repeat(1, box_subj.shape[0], 1)
box_obj = box_obj.unsqueeze(0).repeat(box_obj.shape[0], 1, 1)
proposal_box_pairs = torch.cat((box_subj.view(-1, 4), box_obj.view(-1, 4)), 1)
idx_subj = torch.arange(box_subj.shape[0]).view(-1, 1, 1).repeat(1, box_obj.shape[0], 1).to(proposals_per_image.bbox.device)
idx_obj = torch.arange(box_obj.shape[0]).view(1, -1, 1).repeat(box_subj.shape[0], 1, 1).to(proposals_per_image.bbox.device)
proposal_idx_pairs = torch.cat((idx_subj.view(-1, 1), idx_obj.view(-1, 1)), 1)
keep_idx = (proposal_idx_pairs[:, 0] != proposal_idx_pairs[:, 1]).nonzero().view(-1)
# if we filter non overlap bounding boxes
if self.cfg.MODEL.ROI_RELATION_HEAD.FILTER_NON_OVERLAP:
ious = boxlist_iou(proposals_per_image, proposals_per_image).view(-1)
ious = ious[keep_idx]
keep_idx = keep_idx[(ious > 0).nonzero().view(-1)]
proposal_idx_pairs = proposal_idx_pairs[keep_idx]
proposal_box_pairs = proposal_box_pairs[keep_idx]
proposal_pairs_per_image = BoxPairList(proposal_box_pairs, proposals_per_image.size, proposals_per_image.mode)
proposal_pairs_per_image.add_field("idx_pairs", proposal_idx_pairs)
proposal_pairs.append(proposal_pairs_per_image)
return proposal_pairs
def _get_proposal_pairs(self, proposals):
proposal_pairs = []
for i, proposals_per_image in enumerate(proposals):
box_subj = proposals_per_image.bbox
box_obj = proposals_per_image.bbox
box_subj = box_subj.unsqueeze(1).repeat(1, box_subj.shape[0], 1)
box_obj = box_obj.unsqueeze(0).repeat(box_obj.shape[0], 1, 1)
proposal_box_pairs = torch.cat(
(box_subj.view(-1, 4), box_obj.view(-1, 4)), 1)
idx_subj = torch.arange(box_subj.shape[0]).view(-1, 1, 1).repeat(
1, box_obj.shape[0], 1).to(proposals_per_image.bbox.device)
idx_obj = torch.arange(box_obj.shape[0]).view(1, -1, 1).repeat(
box_subj.shape[0], 1, 1).to(proposals_per_image.bbox.device)
proposal_idx_pairs = torch.cat(
(idx_subj.view(-1, 1), idx_obj.view(-1, 1)), 1)
non_duplicate_idx = (proposal_idx_pairs[:, 0] !=
proposal_idx_pairs[:, 1]).nonzero()
proposal_idx_pairs = proposal_idx_pairs[non_duplicate_idx.view(-1)]
proposal_box_pairs = proposal_box_pairs[non_duplicate_idx.view(-1)]
proposal_pairs_per_image = BoxPairList(proposal_box_pairs,
proposals_per_image.size,
proposals_per_image.mode)
proposal_pairs_per_image.add_field("idx_pairs", proposal_idx_pairs)
proposal_pairs.append(proposal_pairs_per_image)
return proposal_pairs
def match_targets_to_proposals(self, proposal, target):
match_quality_matrix = boxlist_iou(target, proposal)
temp = []
target_box_pairs = []
for i in range(match_quality_matrix.shape[0]):
for j in range(match_quality_matrix.shape[0]):
match_i = match_quality_matrix[i].view(1, -1)
match_j = match_quality_matrix[j].view(-1, 1)
match_ij = (match_i + match_j) / 2
match_ij.view(-1)[::match_quality_matrix.shape[1]] = 0
temp.append(match_ij)
boxi = target.bbox[i]
boxj = target.bbox[j]
box_pair = torch.cat((boxi, boxj), 0)
target_box_pairs.append(box_pair)
match_pair_quality_matrix = torch.stack(temp, 0).view(len(temp), -1)
target_box_pairs = torch.stack(target_box_pairs, 0)
target_pair = BoxPairList(target_box_pairs, target.size, target.mode)
target_pair.add_field("labels",
target.get_field("pred_labels").view(-1))
box_subj = proposal.bbox
box_obj = proposal.bbox
box_subj = box_subj.unsqueeze(1).repeat(1, box_subj.shape[0], 1)
box_obj = box_obj.unsqueeze(0).repeat(box_obj.shape[0], 1, 1)
proposal_box_pairs = torch.cat(
(box_subj.view(-1, 4), box_obj.view(-1, 4)), 1)
proposal_pairs = BoxPairList(proposal_box_pairs, proposal.size,
proposal.mode)
idx_subj = torch.arange(box_subj.shape[0]).view(-1, 1, 1).repeat(
1, box_obj.shape[0], 1).to(proposal.bbox.device)
idx_obj = torch.arange(box_obj.shape[0]).view(1, -1, 1).repeat(
box_subj.shape[0], 1, 1).to(proposal.bbox.device)
proposal_idx_pairs = torch.cat(
(idx_subj.view(-1, 1), idx_obj.view(-1, 1)), 1)
proposal_pairs.add_field("idx_pairs", proposal_idx_pairs)
# matched_idxs = self.proposal_matcher(match_quality_matrix)
matched_idxs = self.proposal_pair_matcher(match_pair_quality_matrix)
# Fast RCNN only need "labels" field for selecting the targets
# target = target.copy_with_fields("pred_labels")
# get the targets corresponding GT for each proposal
# NB: need to clamp the indices because we can have a single
# GT in the image, and matched_idxs can be -2, which goes
# out of bounds
if self.use_matched_pairs_only and (
matched_idxs >= 0).sum() > self.minimal_matched_pairs:
# filter all matched_idxs < 0
proposal_pairs = proposal_pairs[matched_idxs >= 0]
matched_idxs = matched_idxs[matched_idxs >= 0]
matched_targets = target_pair[matched_idxs.clamp(min=0)]
matched_targets.add_field("matched_idxs", matched_idxs)
return matched_targets, proposal_pairs
def prepare_boxpairlist(self, boxes, scores, image_shape):
"""
Returns BoxList from `boxes` and adds probability scores information
as an extra field
`boxes` has shape (#detections, 4 * #classes), where each row represents
a list of predicted bounding boxes for each of the object classes in the
dataset (including the background class). The detections in each row
originate from the same object proposal.
`scores` has shape (#detection, #classes), where each row represents a list
of object detection confidence scores for each of the object classes in the
dataset (including the background class). `scores[i, j]`` corresponds to the
box at `boxes[i, j * 4:(j + 1) * 4]`.
"""
boxes = boxes.reshape(-1, 8)
scores = scores.reshape(-1)
boxlist = BoxPairList(boxes, image_shape, mode="xyxy")
boxlist.add_field("scores", scores)
return boxlist
def _fullsample_test(self, proposals):
"""
This method get all subject-object pairs, and return the proposals.
Note: this function keeps a state.
Arguments:
proposals (list[BoxList])
"""
proposal_pairs = []
for i, proposals_per_image in enumerate(proposals):
box_subj = proposals_per_image.bbox
box_obj = proposals_per_image.bbox
box_subj = box_subj.unsqueeze(1).repeat(1, box_subj.shape[0], 1)
box_obj = box_obj.unsqueeze(0).repeat(box_obj.shape[0], 1, 1)
proposal_box_pairs = torch.cat(
(box_subj.view(-1, 4), box_obj.view(-1, 4)), 1)
idx_subj = torch.arange(box_subj.shape[0]).view(-1, 1, 1).repeat(
1, box_obj.shape[0], 1).to(proposals_per_image.bbox.device)
idx_obj = torch.arange(box_obj.shape[0]).view(1, -1, 1).repeat(
box_subj.shape[0], 1, 1).to(proposals_per_image.bbox.device)
proposal_idx_pairs = torch.cat(
(idx_subj.view(-1, 1), idx_obj.view(-1, 1)), 1)
keep_idx = (proposal_idx_pairs[:, 0] !=
proposal_idx_pairs[:, 1]).nonzero().view(-1)
# if we filter non overlap bounding boxes
if self.cfg.MODEL.ROI_RELATION_HEAD.FILTER_NON_OVERLAP:
ious = boxlist_iou(proposals_per_image,
proposals_per_image).view(-1)
ious = ious[keep_idx]
keep_idx = keep_idx[(ious > 0).nonzero().view(-1)]
proposal_idx_pairs = proposal_idx_pairs[keep_idx]
proposal_box_pairs = proposal_box_pairs[keep_idx]
proposal_pairs_per_image = BoxPairList(proposal_box_pairs,
proposals_per_image.size,
proposals_per_image.mode)
proposal_pairs_per_image.add_field("idx_pairs", proposal_idx_pairs)
proposal_pairs.append(proposal_pairs_per_image)
return proposal_pairs