def cc_fast_nms(self,
boxes,
masks,
scores,
iou_threshold: float = 0.5,
top_k: int = 200):
# Collapse all the classes into 1
scores, classes = scores.max(dim=0)
_, idx = scores.sort(0, descending=True)
idx = idx[:top_k]
boxes_idx = boxes[idx]
# Compute the pairwise IoU between the boxes
iou = jaccard(boxes_idx, boxes_idx)
# Zero out the lower triangle of the cosine similarity matrix and diagonal
iou.triu_(diagonal=1)
# Now that everything in the diagonal and below is zeroed out, if we take the max
# of the IoU matrix along the columns, each column will represent the maximum IoU
# between this element and every element with a higher score than this element.
iou_max, _ = torch.max(iou, dim=0)
# Now just filter out the ones greater than the threshold, i.e., only keep boxes that
# don't have a higher scoring box that would supress it in normal NMS.
idx_out = idx[iou_max <= iou_threshold]
return boxes[idx_out], masks[idx_out], classes[idx_out], scores[
idx_out]
def fast_nms(
self,
boxes,
masks,
scores,
iou_threshold: float = 0.5,
top_k: int = 200,
second_threshold: bool = False,
):
scores, idx = scores.sort(1, descending=True)
idx = idx[:, :top_k].contiguous()
scores = scores[:, :top_k]
num_classes, num_dets = idx.size()
boxes = boxes[idx.view(-1), :].view(num_classes, num_dets, 4)
masks = masks[idx.view(-1), :].view(num_classes, num_dets, -1)
iou = jaccard(boxes, boxes)
iou.triu_(diagonal=1)
iou_max, _ = iou.max(dim=1)
# Now just filter out the ones higher than the threshold
keep = iou_max <= iou_threshold
# We should also only keep detections over the confidence threshold, but at the cost of
# maxing out your detection count for every image, you can just not do that. Because we
# have such a minimal amount of computation per detection (matrix mulitplication only),
# this increase doesn't affect us much (+0.2 mAP for 34 -> 33 fps), so we leave it out.
# However, when you implement this in your method, you should do this second threshold.
if second_threshold:
keep *= scores > self.conf_thresh
# Assign each kept detection to its corresponding class
classes = torch.arange(num_classes,
device=boxes.device)[:, None].expand_as(keep)
classes = classes[keep]
boxes = boxes[keep]
masks = masks[keep]
scores = scores[keep]
# Only keep the top cfg.max_num_detections highest scores across all classes
scores, idx = scores.sort(0, descending=True)
idx = idx[:self.cfg.max_num_detections]
scores = scores[:self.cfg.max_num_detections]
classes = classes[idx]
boxes = boxes[idx]
masks = masks[idx]
return boxes, masks, classes, scores
def _bbox_iou(bbox1, bbox2, iscrowd=False):
with timer.env('BBox IoU'):
ret = jaccard(bbox1, bbox2, iscrowd)
return ret.cpu()
