Source code for torchvision.ops.boxes
import torch
from torch import Tensor
from typing import Tuple
from ._box_convert import _box_cxcywh_to_xyxy, _box_xyxy_to_cxcywh, _box_xywh_to_xyxy, _box_xyxy_to_xywh
import torchvision
from torchvision.extension import _assert_has_ops
[docs]def nms(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor:
"""
Performs non-maximum suppression (NMS) on the boxes according
to their intersection-over-union (IoU).
NMS iteratively removes lower scoring boxes which have an
IoU greater than iou_threshold with another (higher scoring)
box.
If multiple boxes have the exact same score and satisfy the IoU
criterion with respect to a reference box, the selected box is
not guaranteed to be the same between CPU and GPU. This is similar
to the behavior of argsort in PyTorch when repeated values are present.
Args:
boxes (Tensor[N, 4])): boxes to perform NMS on. They
are expected to be in ``(x1, y1, x2, y2)`` format with ``0 <= x1 < x2`` and
``0 <= y1 < y2``.
scores (Tensor[N]): scores for each one of the boxes
iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold
Returns:
Tensor: int64 tensor with the indices of the elements that have been kept
by NMS, sorted in decreasing order of scores
"""
_assert_has_ops()
return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
[docs]def batched_nms(
boxes: Tensor,
scores: Tensor,
idxs: Tensor,
iou_threshold: float,
) -> Tensor:
"""
Performs 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 with ``0 <= x1 < x2`` and
``0 <= y1 < y2``.
scores (Tensor[N]): scores for each one of the boxes
idxs (Tensor[N]): indices of the categories for each one of the boxes.
iou_threshold (float): discards all overlapping boxes with IoU > iou_threshold
Returns:
Tensor: int64 tensor with the indices of the elements that have been kept by NMS, sorted
in decreasing order of scores
"""
# Benchmarks that drove the following thresholds are at
# https://github.com/pytorch/vision/issues/1311#issuecomment-781329339
# Ideally for GPU we'd use a higher threshold
if boxes.numel() > 4_000 and not torchvision._is_tracing():
return _batched_nms_vanilla(boxes, scores, idxs, iou_threshold)
else:
return _batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold)
@torch.jit._script_if_tracing
def _batched_nms_coordinate_trick(
boxes: Tensor,
scores: Tensor,
idxs: Tensor,
iou_threshold: float,
) -> Tensor:
# 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
if boxes.numel() == 0:
return torch.empty((0,), dtype=torch.int64, device=boxes.device)
max_coordinate = boxes.max()
offsets = idxs.to(boxes) * (max_coordinate + torch.tensor(1).to(boxes))
boxes_for_nms = boxes + offsets[:, None]
keep = nms(boxes_for_nms, scores, iou_threshold)
return keep
@torch.jit._script_if_tracing
def _batched_nms_vanilla(
boxes: Tensor,
scores: Tensor,
idxs: Tensor,
iou_threshold: float,
) -> Tensor:
# Based on Detectron2 implementation, just manually call nms() on each class independently
keep_mask = torch.zeros_like(scores, dtype=torch.bool)
for class_id in torch.unique(idxs):
curr_indices = torch.where(idxs == class_id)[0]
curr_keep_indices = nms(boxes[curr_indices], scores[curr_indices], iou_threshold)
keep_mask[curr_indices[curr_keep_indices]] = True
keep_indices = torch.where(keep_mask)[0]
return keep_indices[scores[keep_indices].sort(descending=True)[1]]
[docs]def remove_small_boxes(boxes: Tensor, min_size: float) -> Tensor:
"""
Remove boxes which contains at least one side smaller than min_size.
Args:
boxes (Tensor[N, 4]): boxes in ``(x1, y1, x2, y2)`` format
with ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
min_size (float): minimum size
Returns:
Tensor[K]: indices of the boxes that have both sides
larger than min_size
"""
ws, hs = boxes[:, 2] - boxes[:, 0], boxes[:, 3] - boxes[:, 1]
keep = (ws >= min_size) & (hs >= min_size)
keep = torch.where(keep)[0]
return keep
[docs]def clip_boxes_to_image(boxes: Tensor, size: Tuple[int, int]) -> Tensor:
"""
Clip boxes so that they lie inside an image of size `size`.
Args:
boxes (Tensor[N, 4]): boxes in ``(x1, y1, x2, y2)`` format
with ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
size (Tuple[height, width]): size of the image
Returns:
Tensor[N, 4]: clipped boxes
"""
dim = boxes.dim()
boxes_x = boxes[..., 0::2]
boxes_y = boxes[..., 1::2]
height, width = size
if torchvision._is_tracing():
boxes_x = torch.max(boxes_x, torch.tensor(0, dtype=boxes.dtype, device=boxes.device))
boxes_x = torch.min(boxes_x, torch.tensor(width, dtype=boxes.dtype, device=boxes.device))
boxes_y = torch.max(boxes_y, torch.tensor(0, dtype=boxes.dtype, device=boxes.device))
boxes_y = torch.min(boxes_y, torch.tensor(height, dtype=boxes.dtype, device=boxes.device))
else:
boxes_x = boxes_x.clamp(min=0, max=width)
boxes_y = boxes_y.clamp(min=0, max=height)
clipped_boxes = torch.stack((boxes_x, boxes_y), dim=dim)
return clipped_boxes.reshape(boxes.shape)
[docs]def box_convert(boxes: Tensor, in_fmt: str, out_fmt: str) -> Tensor:
"""
Converts boxes from given in_fmt to out_fmt.
Supported in_fmt and out_fmt are:
'xyxy': boxes are represented via corners, x1, y1 being top left and x2, y2 being bottom right.
This is the format that torchvision utilities expect.
'xywh' : boxes are represented via corner, width and height, x1, y2 being top left, w, h being width and height.
'cxcywh' : boxes are represented via centre, width and height, cx, cy being center of box, w, h
being width and height.
Args:
boxes (Tensor[N, 4]): boxes which will be converted.
in_fmt (str): Input format of given boxes. Supported formats are ['xyxy', 'xywh', 'cxcywh'].
out_fmt (str): Output format of given boxes. Supported formats are ['xyxy', 'xywh', 'cxcywh']
Returns:
Tensor[N, 4]: Boxes into converted format.
"""
allowed_fmts = ("xyxy", "xywh", "cxcywh")
if in_fmt not in allowed_fmts or out_fmt not in allowed_fmts:
raise ValueError("Unsupported Bounding Box Conversions for given in_fmt and out_fmt")
if in_fmt == out_fmt:
return boxes.clone()
if in_fmt != 'xyxy' and out_fmt != 'xyxy':
# convert to xyxy and change in_fmt xyxy
if in_fmt == "xywh":
boxes = _box_xywh_to_xyxy(boxes)
elif in_fmt == "cxcywh":
boxes = _box_cxcywh_to_xyxy(boxes)
in_fmt = 'xyxy'
if in_fmt == "xyxy":
if out_fmt == "xywh":
boxes = _box_xyxy_to_xywh(boxes)
elif out_fmt == "cxcywh":
boxes = _box_xyxy_to_cxcywh(boxes)
elif out_fmt == "xyxy":
if in_fmt == "xywh":
boxes = _box_xywh_to_xyxy(boxes)
elif in_fmt == "cxcywh":
boxes = _box_cxcywh_to_xyxy(boxes)
return boxes
def _upcast(t: Tensor) -> Tensor:
# Protects from numerical overflows in multiplications by upcasting to the equivalent higher type
if t.is_floating_point():
return t if t.dtype in (torch.float32, torch.float64) else t.float()
else:
return t if t.dtype in (torch.int32, torch.int64) else t.int()
[docs]def box_area(boxes: Tensor) -> Tensor:
"""
Computes the area of a set of bounding boxes, which are specified by their
(x1, y1, x2, y2) coordinates.
Args:
boxes (Tensor[N, 4]): boxes for which the area will be computed. They
are expected to be in (x1, y1, x2, y2) format with
``0 <= x1 < x2`` and ``0 <= y1 < y2``.
Returns:
Tensor[N]: the area for each box
"""
boxes = _upcast(boxes)
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
# with slight modifications
def _box_inter_union(boxes1: Tensor, boxes2: Tensor) -> Tuple[Tensor, Tensor]:
area1 = box_area(boxes1)
area2 = box_area(boxes2)
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
wh = _upcast(rb - lt).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
union = area1[:, None] + area2 - inter
return inter, union
[docs]def box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
"""
Return intersection-over-union (Jaccard index) between two sets of boxes.
Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
``0 <= x1 < x2`` and ``0 <= y1 < y2``.
Args:
boxes1 (Tensor[N, 4]): first set of boxes
boxes2 (Tensor[M, 4]): second set of boxes
Returns:
Tensor[N, M]: the NxM matrix containing the pairwise IoU values for every element in boxes1 and boxes2
"""
inter, union = _box_inter_union(boxes1, boxes2)
iou = inter / union
return iou
# Implementation adapted from https://github.com/facebookresearch/detr/blob/master/util/box_ops.py
[docs]def generalized_box_iou(boxes1: Tensor, boxes2: Tensor) -> Tensor:
"""
Return generalized intersection-over-union (Jaccard index) between two sets of boxes.
Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
``0 <= x1 < x2`` and ``0 <= y1 < y2``.
Args:
boxes1 (Tensor[N, 4]): first set of boxes
boxes2 (Tensor[M, 4]): second set of boxes
Returns:
Tensor[N, M]: the NxM matrix containing the pairwise generalized IoU values
for every element in boxes1 and boxes2
"""
# degenerate boxes gives inf / nan results
# so do an early check
assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
inter, union = _box_inter_union(boxes1, boxes2)
iou = inter / union
lti = torch.min(boxes1[:, None, :2], boxes2[:, :2])
rbi = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
whi = _upcast(rbi - lti).clamp(min=0) # [N,M,2]
areai = whi[:, :, 0] * whi[:, :, 1]
return iou - (areai - union) / areai
