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""" PyTorch Soft-NMS
This code was adapted from a PR for detectron2 submitted by https://github.com/alekseynp
https://github.com/facebookresearch/detectron2/pull/1183/files
Detectron2 is licensed Apache 2.0, Copyright Facebook Inc.
"""
import torch
from typing import List
def pairwise_iou(boxes1, boxes2) -> torch.Tensor:
"""
Given two lists of boxes of size N and M,
compute the IoU (intersection over union)
between __all__ N x M pairs of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Args:
boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.
Returns:
Tensor: IoU, sized [N,M].
"""
area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1]) # [N,]
area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1]) # [M,]
width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(
boxes1[:, None, :2], boxes2[:, :2]
) # [N,M,2]
width_height.clamp_(min=0) # [N,M,2]
inter = width_height.prod(dim=2) # [N,M]
# handle empty boxes
iou = torch.where(
inter > 0,
inter / (area1[:, None] + area2 - inter),
torch.zeros(1, dtype=inter.dtype, device=inter.device),
)
return iou
def soft_nms(
boxes,
scores,
method_gaussian: bool = True,
sigma: float = 0.5,
iou_threshold: float = .5,
score_threshold: float = 0.005
):
"""
Soft non-max suppression algorithm.
Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec]
(https://arxiv.org/abs/1704.04503)
Args:
boxes_remain (Tensor[N, ?]):
boxes where NMS will be performed
if Boxes, in (x1, y1, x2, y2) format
if RotatedBoxes, in (x_ctr, y_ctr, width, height, angle_degrees) format
scores_remain (Tensor[N]):
scores for each one of the boxes
method_gaussian (bool): use gaussian method if True, otherwise linear
sigma (float):
parameter for Gaussian penalty function
iou_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
score_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept
"""
device = boxes.device
boxes_remain = boxes.clone()
scores_remain = scores.clone()
num_elem = scores_remain.size()[0]
idxs = torch.arange(num_elem)
idxs_out = torch.zeros(num_elem, dtype=torch.int64, device=device)
scores_out = torch.zeros(num_elem, dtype=torch.float32, device=device)
count: int = 0
while scores_remain.numel() > 0:
top_idx = torch.argmax(scores_remain)
idxs_out[count] = idxs[top_idx]
scores_out[count] = scores_remain[top_idx]
count += 1
top_box = boxes_remain[top_idx]
ious = pairwise_iou(top_box.unsqueeze(0), boxes_remain)[0]
if method_gaussian:
decay = torch.exp(-torch.pow(ious, 2) / sigma)
else:
decay = torch.ones_like(ious)
decay_mask = ious > iou_threshold
decay[decay_mask] = 1 - ious[decay_mask]
scores_remain *= decay
keep = scores_remain > score_threshold
keep[top_idx] = torch.tensor(False, device=device)
boxes_remain = boxes_remain[keep]
scores_remain = scores_remain[keep]
idxs = idxs[keep]
return idxs_out[:count], scores_out[:count]
def batched_soft_nms(
boxes, scores, idxs,
method_gaussian: bool = True,
sigma: float = 0.5,
iou_threshold: float = .5,
score_threshold: float = 0.001):
"""
Performs soft non-maximum suppression in a batched fashion.
Each index value correspond to a category, and NMS
will not be applied between elements of different categories.
Args:
boxes (Tensor[N, 4]):
boxes where NMS will be performed. They
are expected to be in (x1, y1, x2, y2) format
scores (Tensor[N]):
scores for each one of the boxes
idxs (Tensor[N]):
indices of the categories for each one of the boxes.
method (str):
one of ['gaussian', 'linear', 'hard']
see paper for details. users encouraged not to use "hard", as this is the
same nms available elsewhere in detectron2
sigma (float):
parameter for Gaussian penalty function
iou_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
score_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept
"""
if boxes.numel() == 0:
return (
torch.empty((0,), dtype=torch.int64, device=boxes.device),
torch.empty((0,), dtype=torch.float32, device=scores.device),
)
# strategy: in order to perform NMS independently per class.
# we add an offset to all the boxes. The offset is dependent
# only on the class idx, and is large enough so that boxes
# from different classes do not overlap
max_coordinate = boxes.max()
offsets = idxs.to(boxes) * (max_coordinate + 1)
boxes_for_nms = boxes + offsets[:, None]
return soft_nms(
boxes_for_nms, scores, method_gaussian=method_gaussian, sigma=sigma,
iou_threshold=iou_threshold, score_threshold=score_threshold
)
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