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# Functions for 1D NMS, modified from:
# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/nms.py
import torch
import nms_1d_cpu
class NMSop(torch.autograd.Function):
@staticmethod
def forward(ctx, segs, scores, cls_idxs, iou_threshold, min_score, max_num):
# vanilla nms will not change the score, so we can filter segs first
is_filtering_by_score = min_score > 0
if is_filtering_by_score:
valid_mask = scores > min_score
segs, scores = segs[valid_mask], scores[valid_mask]
cls_idxs = cls_idxs[valid_mask]
valid_inds = torch.nonzero(valid_mask, as_tuple=False).squeeze(dim=1)
# nms op; return inds that is sorted by descending order
inds = nms_1d_cpu.nms(
segs.contiguous().cpu(),
scores.contiguous().cpu(),
iou_threshold=float(iou_threshold),
)
# cap by max number
if max_num > 0:
inds = inds[: min(max_num, len(inds))]
# return the sorted segs / scores
sorted_segs = segs[inds]
sorted_scores = scores[inds]
sorted_cls_idxs = cls_idxs[inds]
return sorted_segs.clone(), sorted_scores.clone(), sorted_cls_idxs.clone()
class SoftNMSop(torch.autograd.Function):
@staticmethod
def forward(ctx, segs, scores, cls_idxs, iou_threshold, sigma, min_score, method, max_num, t1, t2):
# pre allocate memory for sorted results
dets = segs.new_empty((segs.size(0), 3), device="cpu")
# softnms op, return dets that stores the sorted segs / scores
inds = nms_1d_cpu.softnms(
segs.cpu(),
scores.cpu(),
dets.cpu(),
iou_threshold=float(iou_threshold),
sigma=float(sigma),
min_score=float(min_score),
method=int(method),
t1=float(t1),
t2=float(t2),
)
# cap by max number
if max_num > 0:
n_segs = min(len(inds), max_num)
else:
n_segs = len(inds)
sorted_segs = dets[:n_segs, :2]
sorted_scores = dets[:n_segs, 2]
sorted_cls_idxs = cls_idxs[inds]
sorted_cls_idxs = sorted_cls_idxs[:n_segs]
return sorted_segs.clone(), sorted_scores.clone(), sorted_cls_idxs.clone()
def seg_voting(nms_segs, all_segs, all_scores, iou_threshold, score_offset=1.5):
"""
blur localization results by incorporating side segs.
this is known as bounding box voting in object detection literature.
slightly boost the performance around iou_threshold
"""
# *_segs : N_i x 2, all_scores: N,
# apply offset
offset_scores = all_scores + score_offset
# computer overlap between nms and all segs
# construct the distance matrix of # N_nms x # N_all
num_nms_segs, num_all_segs = nms_segs.shape[0], all_segs.shape[0]
ex_nms_segs = nms_segs[:, None].expand(num_nms_segs, num_all_segs, 2)
ex_all_segs = all_segs[None, :].expand(num_nms_segs, num_all_segs, 2)
# compute intersection
left = torch.maximum(ex_nms_segs[:, :, 0], ex_all_segs[:, :, 0])
right = torch.minimum(ex_nms_segs[:, :, 1], ex_all_segs[:, :, 1])
inter = (right - left).clamp(min=0)
# lens of all segments
nms_seg_lens = ex_nms_segs[:, :, 1] - ex_nms_segs[:, :, 0]
all_seg_lens = ex_all_segs[:, :, 1] - ex_all_segs[:, :, 0]
# iou
iou = inter / (nms_seg_lens + all_seg_lens - inter)
# get neighbors (# N_nms x # N_all) / weights
seg_weights = (iou >= iou_threshold).to(all_scores.dtype) * all_scores[None, :]
seg_weights /= torch.sum(seg_weights, dim=1, keepdim=True)
refined_segs = seg_weights @ all_segs
return refined_segs
def batched_nms(
segs,
scores,
cls_idxs,
iou_threshold=0.0, # does not matter when use soft nms
min_score=0.0,
max_seg_num=100,
use_soft_nms=True,
multiclass=True,
sigma=0.5,
voting_thresh=0.0, # set 0 to disable
method=2, # 0: vanilla nms, 1: linear, 2: gaussian, 3: improved gaussian
t1=0, # only used in improved gaussian for better recall
t2=0, # only used in improved gaussian for better recall
):
# method 2 means we use gaussian soft-nms, so iou_threshold does not matter
# make sure the inputs are float
segs = segs.float()
scores = scores.float()
# Based on Detectron2 implementation,
num_segs = segs.shape[0]
# corner case, no prediction outputs
if num_segs == 0:
return (
torch.zeros([0, 2]),
torch.zeros([0]),
torch.zeros([0], dtype=cls_idxs.dtype),
)
if multiclass:
# multiclass nms: apply nms on each class independently
new_segs, new_scores, new_cls_idxs = [], [], []
for class_id in torch.unique(cls_idxs):
curr_indices = torch.where(cls_idxs == class_id)[0]
# soft_nms vs nms
if use_soft_nms:
sorted_segs, sorted_scores, sorted_cls_idxs = SoftNMSop.apply(
segs[curr_indices],
scores[curr_indices],
cls_idxs[curr_indices],
iou_threshold,
sigma,
min_score,
method,
max_seg_num,
t1,
t2,
)
else:
sorted_segs, sorted_scores, sorted_cls_idxs = NMSop.apply(
segs[curr_indices],
scores[curr_indices],
cls_idxs[curr_indices],
iou_threshold,
min_score,
max_seg_num,
)
# disable seg voting for multiclass nms, no sufficient segs
# fill in the class index
new_segs.append(sorted_segs)
new_scores.append(sorted_scores)
new_cls_idxs.append(sorted_cls_idxs)
# cat the results
new_segs = torch.cat(new_segs)
new_scores = torch.cat(new_scores)
new_cls_idxs = torch.cat(new_cls_idxs)
else:
# class agnostic
if use_soft_nms:
new_segs, new_scores, new_cls_idxs = SoftNMSop.apply(
segs,
scores,
cls_idxs,
iou_threshold,
sigma,
min_score,
method,
max_seg_num,
t1,
t2,
)
else:
new_segs, new_scores, new_cls_idxs = NMSop.apply(
segs,
scores,
cls_idxs,
iou_threshold,
min_score,
max_seg_num,
)
# seg voting
if voting_thresh > 0:
new_segs = seg_voting(new_segs, segs, scores, voting_thresh)
# sort based on scores and return
# truncate the results based on max_seg_num
_, idxs = new_scores.sort(descending=True)
max_seg_num = min(max_seg_num, new_segs.shape[0])
# needed for multiclass NMS
new_segs = new_segs[idxs[:max_seg_num]]
new_scores = new_scores[idxs[:max_seg_num]]
new_cls_idxs = new_cls_idxs[idxs[:max_seg_num]]
return new_segs, new_scores, new_cls_idxs
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