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import torch
from detectron2.structures import Boxes, RotatedBoxes, pairwise_iou, pairwise_iou_rotated
""" Soft-NMS Pull request from: https://github.com/facebookresearch/detectron2/pull/1183/files
"""
def soft_nms(boxes, scores, method, gaussian_sigma, linear_threshold, prune_threshold):
"""
Performs soft non-maximum suppression algorithm on axis aligned boxes
Args:
boxes (Tensor[N, 5]):
boxes where NMS will be performed. They
are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format
scores (Tensor[N]):
scores 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
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_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
"""
return _soft_nms(
Boxes,
pairwise_iou,
boxes,
scores,
method,
gaussian_sigma,
linear_threshold,
prune_threshold,
)
def soft_nms_rotated(boxes, scores, method, gaussian_sigma, linear_threshold, prune_threshold):
"""
Performs soft non-maximum suppression algorithm on rotated boxes
Args:
boxes (Tensor[N, 5]):
boxes where NMS will be performed. They
are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format
scores (Tensor[N]):
scores 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
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_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 """
return _soft_nms(
RotatedBoxes,
pairwise_iou_rotated,
boxes,
scores,
method,
gaussian_sigma,
linear_threshold,
prune_threshold,
)
def batched_soft_nms(
boxes, scores, idxs, method, gaussian_sigma, linear_threshold, prune_threshold
):
"""
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
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_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_sigma, linear_threshold, prune_threshold
)
def batched_soft_nms_rotated(
boxes, scores, idxs, method, gaussian_sigma, linear_threshold, prune_threshold
):
"""
Performs soft non-maximum suppression in a batched fashion on rotated bounding boxes.
Each index value correspond to a category, and NMS
will not be applied between elements of different categories.
Args:
boxes (Tensor[N, 5]):
boxes where NMS will be performed. They
are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) 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
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_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[:, :2].max() + torch.norm(boxes[:, 2:4], 2, dim=1).max()
offsets = idxs.to(boxes) * (max_coordinate + 1)
boxes_for_nms = boxes.clone()
boxes_for_nms[:, :2] += offsets[:, None]
return soft_nms_rotated(
boxes_for_nms, scores, method, gaussian_sigma, linear_threshold, prune_threshold
)
def _soft_nms(
box_class,
pairwise_iou_func,
boxes,
scores,
method,
gaussian_sigma,
linear_threshold,
prune_threshold,
):
"""
Soft non-max suppression algorithm.
Implementation of [Soft-NMS -- Improving Object Detection With One Line of Codec]
(https://arxiv.org/abs/1704.04503)
Args:
box_class (cls): one of Box, RotatedBoxes
pairwise_iou_func (func): one of pairwise_iou, pairwise_iou_rotated
boxes (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 (Tensor[N]):
scores 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
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_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
"""
boxes = boxes.clone()
scores = scores.clone()
idxs = torch.arange(scores.size()[0])
idxs_out = []
scores_out = []
while scores.numel() > 0:
top_idx = torch.argmax(scores)
idxs_out.append(idxs[top_idx].item())
scores_out.append(scores[top_idx].item())
top_box = boxes[top_idx]
ious = pairwise_iou_func(box_class(top_box.unsqueeze(0)), box_class(boxes))[0]
if method == "linear":
decay = torch.ones_like(ious)
decay_mask = ious > linear_threshold
decay[decay_mask] = 1 - ious[decay_mask]
elif method == "gaussian":
decay = torch.exp(-torch.pow(ious, 2) / gaussian_sigma)
elif method == "hard": # standard NMS
decay = (ious < linear_threshold).float()
else:
raise NotImplementedError("{} soft nms method not implemented.".format(method))
scores *= decay
keep = scores > prune_threshold
keep[top_idx] = False
boxes = boxes[keep]
scores = scores[keep]
idxs = idxs[keep]
return torch.tensor(idxs_out).to(boxes.device), torch.tensor(scores_out).to(scores.device) |