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# mmcv.ops.diff_iou_rotated 源代码

# Copyright (c) OpenMMLab. All rights reserved.
# Adapted from https://github.com/lilanxiao/Rotated_IoU/blob/master/box_intersection_2d.py  # noqa
# Adapted from https://github.com/lilanxiao/Rotated_IoU/blob/master/oriented_iou_loss.py  # noqa
from typing import Tuple

import torch
from torch import Tensor

EPSILON = 1e-8
['diff_iou_rotated_sort_vertices_forward'])

class SortVertices(Function):

@staticmethod
idx = ext_module.diff_iou_rotated_sort_vertices_forward(
if torch.__version__ != 'parrots':
ctx.mark_non_differentiable(idx)
return idx

@staticmethod
return ()

def box_intersection(corners1: Tensor,
corners2: Tensor) -> Tuple[Tensor, Tensor]:
"""Find intersection points of rectangles.
Convention: if two edges are collinear, there is no intersection point.

Args:
corners1 (Tensor): (B, N, 4, 2) First batch of boxes.
corners2 (Tensor): (B, N, 4, 2) Second batch of boxes.

Returns:
Tuple:
- Tensor: (B, N, 4, 4, 2) Intersections.
- Tensor: (B, N, 4, 4) Valid intersections mask.
"""
# build edges from corners
# B, N, 4, 4: Batch, Box, edge, point
line1 = torch.cat([corners1, corners1[:, :, [1, 2, 3, 0], :]], dim=3)
line2 = torch.cat([corners2, corners2[:, :, [1, 2, 3, 0], :]], dim=3)
# duplicate data to pair each edges from the boxes
# (B, N, 4, 4) -> (B, N, 4, 4, 4) : Batch, Box, edge1, edge2, point
line1_ext = line1.unsqueeze(3)
line2_ext = line2.unsqueeze(2)
x1, y1, x2, y2 = line1_ext.split([1, 1, 1, 1], dim=-1)
x3, y3, x4, y4 = line2_ext.split([1, 1, 1, 1], dim=-1)
# math: https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection
numerator = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
denumerator_t = (x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)
t = denumerator_t / numerator
t[numerator == .0] = -1.
mask_t = (t > 0) & (t < 1)  # intersection on line segment 1
denumerator_u = (x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)
u = -denumerator_u / numerator
u[numerator == .0] = -1.
mask_u = (u > 0) & (u < 1)  # intersection on line segment 2
# overwrite with EPSILON. otherwise numerically unstable
t = denumerator_t / (numerator + EPSILON)
intersections = torch.stack([x1 + t * (x2 - x1), y1 + t * (y2 - y1)],
dim=-1)

def box1_in_box2(corners1: Tensor, corners2: Tensor) -> Tensor:
"""Check if corners of box1 lie in box2.
Convention: if a corner is exactly on the edge of the other box,
it's also a valid point.

Args:
corners1 (Tensor): (B, N, 4, 2) First batch of boxes.
corners2 (Tensor): (B, N, 4, 2) Second batch of boxes.

Returns:
Tensor: (B, N, 4) Intersection.
"""
# a, b, c, d - 4 vertices of box2
a = corners2[:, :, 0:1, :]  # (B, N, 1, 2)
b = corners2[:, :, 1:2, :]  # (B, N, 1, 2)
d = corners2[:, :, 3:4, :]  # (B, N, 1, 2)
# ab, am, ad - vectors between corresponding vertices
ab = b - a  # (B, N, 1, 2)
am = corners1 - a  # (B, N, 4, 2)
ad = d - a  # (B, N, 1, 2)
prod_ab = torch.sum(ab * am, dim=-1)  # (B, N, 4)
norm_ab = torch.sum(ab * ab, dim=-1)  # (B, N, 1)
# NOTE: the expression looks ugly but is stable if the two boxes
# are exactly the same also stable with different scale of bboxes
cond1 = (prod_ab / norm_ab > -1e-6) * (prod_ab / norm_ab < 1 + 1e-6
)  # (B, N, 4)
)  # (B, N, 4)
return cond1 * cond2

def box_in_box(corners1: Tensor, corners2: Tensor) -> Tuple[Tensor, Tensor]:
"""Check if corners of two boxes lie in each other.

Args:
corners1 (Tensor): (B, N, 4, 2) First batch of boxes.
corners2 (Tensor): (B, N, 4, 2) Second batch of boxes.

Returns:
Tuple:
- Tensor: (B, N, 4) True if i-th corner of box1 is in box2.
- Tensor: (B, N, 4) True if i-th corner of box2 is in box1.
"""
c1_in_2 = box1_in_box2(corners1, corners2)
c2_in_1 = box1_in_box2(corners2, corners1)
return c1_in_2, c2_in_1

def build_vertices(corners1: Tensor, corners2: Tensor, c1_in_2: Tensor,
c2_in_1: Tensor, intersections: Tensor,
"""Find vertices of intersection area.

Args:
corners1 (Tensor): (B, N, 4, 2) First batch of boxes.
corners2 (Tensor): (B, N, 4, 2) Second batch of boxes.
c1_in_2 (Tensor): (B, N, 4) True if i-th corner of box1 is in box2.
c2_in_1 (Tensor): (B, N, 4) True if i-th corner of box2 is in box1.
intersections (Tensor): (B, N, 4, 4, 2) Intersections.

Returns:
Tuple:
- Tensor: (B, N, 24, 2) Vertices of intersection area;
only some elements are valid.
- Tensor: (B, N, 24) Mask of valid elements in vertices.
"""
# NOTE: inter has elements equals zero and has zeros gradient
# (masked by multiplying with 0); can be used as trick
B = corners1.size()[0]
N = corners1.size()[1]
# (B, N, 4 + 4 + 16, 2)
vertices = torch.cat(
[corners1, corners2,
intersections.view([B, N, -1, 2])], dim=2)
# Bool (B, N, 4 + 4 + 16)

def sort_indices(vertices: Tensor, mask: Tensor) -> Tensor:
"""Sort indices.
Note:
why 9? the polygon has maximal 8 vertices.
+1 to duplicate the first element.
the index should have following structure:
(A, B, C, ... , A, X, X, X)
and X indicates the index of arbitrary elements in the last
16 (intersections not corners) with value 0 and mask False.
(cause they have zero value and zero gradient)

Args:
vertices (Tensor): (B, N, 24, 2) Box vertices.

Returns:
Tensor: (B, N, 9) Sorted indices.

"""
num_valid = torch.sum(mask.int(), dim=2).int()  # (B, N)
mean = torch.sum(
keepdim=True) / num_valid.unsqueeze(-1).unsqueeze(-1)
vertices_normalized = vertices - mean  # normalization makes sorting easier

def calculate_area(idx_sorted: Tensor,
vertices: Tensor) -> Tuple[Tensor, Tensor]:
"""Calculate area of intersection.

Args:
idx_sorted (Tensor): (B, N, 9) Sorted vertex ids.
vertices (Tensor): (B, N, 24, 2) Vertices.

Returns:
Tuple:
- Tensor (B, N): Area of intersection.
- Tensor: (B, N, 9, 2) Vertices of polygon with zero padding.
"""
idx_ext = idx_sorted.unsqueeze(-1).repeat([1, 1, 1, 2])
selected = torch.gather(vertices, 2, idx_ext)
total = selected[:, :, 0:-1, 0] * selected[:, :, 1:, 1] \
- selected[:, :, 0:-1, 1] * selected[:, :, 1:, 0]
total = torch.sum(total, dim=2)
area = torch.abs(total) / 2
return area, selected

def oriented_box_intersection_2d(corners1: Tensor,
corners2: Tensor) -> Tuple[Tensor, Tensor]:
"""Calculate intersection area of 2d rotated boxes.

Args:
corners1 (Tensor): (B, N, 4, 2) First batch of boxes.
corners2 (Tensor): (B, N, 4, 2) Second batch of boxes.

Returns:
Tuple:
- Tensor (B, N): Area of intersection.
- Tensor (B, N, 9, 2): Vertices of polygon with zero padding.
"""
c12, c21 = box_in_box(corners1, corners2)
vertices, mask = build_vertices(corners1, corners2, c12, c21,
return calculate_area(sorted_indices, vertices)

def box2corners(box: Tensor) -> Tensor:
"""Convert rotated 2d box coordinate to corners.

Args:
box (Tensor): (B, N, 5) with x, y, w, h, alpha.

Returns:
Tensor: (B, N, 4, 2) Corners.
"""
B = box.size()[0]
x, y, w, h, alpha = box.split([1, 1, 1, 1, 1], dim=-1)
x4 = box.new_tensor([0.5, -0.5, -0.5, 0.5]).to(box.device)
x4 = x4 * w  # (B, N, 4)
y4 = box.new_tensor([0.5, 0.5, -0.5, -0.5]).to(box.device)
y4 = y4 * h  # (B, N, 4)
corners = torch.stack([x4, y4], dim=-1)  # (B, N, 4, 2)
sin = torch.sin(alpha)
cos = torch.cos(alpha)
row1 = torch.cat([cos, sin], dim=-1)
row2 = torch.cat([-sin, cos], dim=-1)  # (B, N, 2)
rot_T = torch.stack([row1, row2], dim=-2)  # (B, N, 2, 2)
rotated = torch.bmm(corners.view([-1, 4, 2]), rot_T.view([-1, 2, 2]))
rotated = rotated.view([B, -1, 4, 2])  # (B * N, 4, 2) -> (B, N, 4, 2)
rotated[..., 0] += x
rotated[..., 1] += y
return rotated

[文档]def diff_iou_rotated_2d(box1: Tensor, box2: Tensor) -> Tensor:
"""Calculate differentiable iou of rotated 2d boxes.

Args:
box1 (Tensor): (B, N, 5) First box.
box2 (Tensor): (B, N, 5) Second box.

Returns:
Tensor: (B, N) IoU.
"""
corners1 = box2corners(box1)
corners2 = box2corners(box2)
intersection, _ = oriented_box_intersection_2d(corners1,
corners2)  # (B, N)
area1 = box1[:, :, 2] * box1[:, :, 3]
area2 = box2[:, :, 2] * box2[:, :, 3]
union = area1 + area2 - intersection
iou = intersection / union
return iou

[文档]def diff_iou_rotated_3d(box3d1: Tensor, box3d2: Tensor) -> Tensor:
"""Calculate differentiable iou of rotated 3d boxes.

Args:
box3d1 (Tensor): (B, N, 3+3+1) First box (x,y,z,w,h,l,alpha).
box3d2 (Tensor): (B, N, 3+3+1) Second box (x,y,z,w,h,l,alpha).

Returns:
Tensor: (B, N) IoU.
"""
box1 = box3d1[..., [0, 1, 3, 4, 6]]  # 2d box
box2 = box3d2[..., [0, 1, 3, 4, 6]]
corners1 = box2corners(box1)
corners2 = box2corners(box2)
intersection, _ = oriented_box_intersection_2d(corners1, corners2)
zmax1 = box3d1[..., 2] + box3d1[..., 5] * 0.5
zmin1 = box3d1[..., 2] - box3d1[..., 5] * 0.5
zmax2 = box3d2[..., 2] + box3d2[..., 5] * 0.5
zmin2 = box3d2[..., 2] - box3d2[..., 5] * 0.5
z_overlap = (torch.min(zmax1, zmax2) -
torch.max(zmin1, zmin2)).clamp_(min=0.)
intersection_3d = intersection * z_overlap
volume1 = box3d1[..., 3] * box3d1[..., 4] * box3d1[..., 5]
volume2 = box3d2[..., 3] * box3d2[..., 4] * box3d2[..., 5]
union_3d = volume1 + volume2 - intersection_3d
return intersection_3d / union_3d


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