def nms(dets, thresh):
"""Dispatch to either CPU or GPU NMS implementations.
used in both inference (keep_out is 1D) and 'proposal_layer' (keep_out is 2D)
Args:
dets: [bs, N, 4]
thresh: nms threshold
Returns:
keep_out: ndarray (bs, min_keep_num)
"""
bs = dets.size(0)
keep = []
min_keep_num = 1e10
for i in range(bs):
curr_sample_keep = pth_nms(dets[i, :], thresh)
keep.append(curr_sample_keep)
if len(curr_sample_keep) < min_keep_num:
min_keep_num = len(curr_sample_keep)
keep_out = np.zeros((bs, min_keep_num), dtype=np.int32)
for i in range(bs):
keep_out[i, :] = keep[i][:min_keep_num].cpu().numpy()
return keep_out
def nms(dets, thresh):
"Dispatch to either CPU or GPU NMS implementations.\
Accept dets as tensor" ""
return pth_nms(dets, thresh)
def nms(dets, thresh): """Dispatch to either CPU or GPU NMS implementations. Accept dets as tensor""" return pth_nms(dets, thresh)
def box_nms(bboxes, scores, threshold=0.5, mode='union'):
dets = torch.cat([bboxes, scores.unsqueeze(1)], dim=1)
return pth_nms(dets, threshold)
