consnet.api.bbox
- consnet.api.bbox.pair_iou(bboxes1, bboxes2)[source]
Compute the intersection-over-unions (IoUs) among human-object pairs.
- Parameters
bboxes1 (
Tensor[N, 8]
) – Human-object pairs to be computed. They are expected to be in(x1, y1, x2, y2, ...)
format.bboxes2 (
Tensor[M, 8]
) – Human-object pairs to be computed. They are expected to be in(x1, y1, x2, y2, ...)
format.
- Returns
The computed pairwise IoU values
- Return type
Tensor[N, M]
- consnet.api.bbox.pair_nms(bboxes, scores, method='fast', hard_thr=0.5, soft_thr=0.3, sigma=0.5, score_thr=1e-06)[source]
Perform non-maximum suppression (NMS) on human-object pairs. This method supports multiple NMS types including Fast NMS [1], Cluster NMS [2], Normal NMS [3] and Soft NMS [4] with linear or gaussian suppression terms.
- Parameters
bboxes (
Tensor[N, 9]
) – Batches of human-object pairs to be suppressed. The values are expected to be in(batch_id, x1, y1, x2, y2, ...)
format.scores (
Tensor[N]
) – Human-object interaction detection scores to be considered.method (str, optional) – Type of NMS. Expected values include
'fast'
,'cluster'
,'normal'
,'linear'
and'gaussian'
, indicating Fast NMS, Cluster NMS, Normal NMS and Soft NMS with linear or gaussian suppression terms.hard_thr (float, optional) – Hard threshold of NMS. This attribute is applied to all NMS methods. Human-object pairs with IoUs higher than this value will be discarded.
soft_thr (float, optional) – Soft threshold of NMS. This attribute is only applied to
linear
andgaussian
methods. Human-object pairs with IoUs lower thanhard_thr
but higher than this value will be suppressed in a soft manner.sigma (float, optional) – Hyperparameter for
gaussian
method.score_thr (float, optional) – Score threshold. This attribute is applied to
normal
,linear
andgaussian
methods. Human-object pairs with suppressed scores lower than this value will be discarded.
- Returns
Human-object pairs and their updated scores after NMS. The values are expected to be in
(batch_id, x1, y1, x2, y2, ..., score)
format.- Return type
Tensor[N, 10]
References
Bolya et al. (https://arxiv.org/abs/1904.02689)
Zheng et al. (https://arxiv.org/abs/2005.03572)
Neubeck er al. (https://doi.org/10.1109/icpr.2006.479)
Bodla et al. (https://arxiv.org/abs/1704.04503)