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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from ..utils import ext_loader
ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps'])
def _bbox_overlaps_cpu(bboxes1: torch.Tensor,
bboxes2: torch.Tensor,
mode: str = 'iou',
aligned: bool = False,
offset: int = 0) -> torch.Tensor:
assert mode in ['iou', 'iof']
if aligned:
lt = torch.max(bboxes1[:, :2], bboxes2[:, :2]) # [rows, 2]
rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:]) # [rows, 2]
wh = (rb - lt + offset).clamp(min=0) # [rows, 2]
overlap = wh[:, 0] * wh[:, 1]
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + offset) * (
bboxes1[:, 3] - bboxes1[:, 1] + offset)
if mode == 'iou':
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + offset) * (
bboxes2[:, 3] - bboxes2[:, 1] + offset)
ious = overlap / (area1 + area2 - overlap)
else:
ious = overlap / area1
else:
lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2]) # [rows, cols, 2]
rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:]) # [rows, cols, 2]
wh = (rb - lt + offset).clamp(min=0) # [rows, cols, 2]
overlap = wh[:, :, 0] * wh[:, :, 1]
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + offset) * (
bboxes1[:, 3] - bboxes1[:, 1] + offset)
if mode == 'iou':
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + offset) * (
bboxes2[:, 3] - bboxes2[:, 1] + offset)
ious = overlap / (area1[:, None] + area2 - overlap)
else:
ious = overlap / (area1[:, None])
return ious
def bbox_overlaps(bboxes1: torch.Tensor,
bboxes2: torch.Tensor,
mode: str = 'iou',
aligned: bool = False,
offset: int = 0) -> torch.Tensor:
"""Calculate overlap between two set of bboxes.
If ``aligned`` is ``False``, then calculate the ious between each bbox
of bboxes1 and bboxes2, otherwise the ious between each aligned pair of
bboxes1 and bboxes2.
Args:
bboxes1 (torch.Tensor): shape (m, 4) in <x1, y1, x2, y2> format or
empty.
bboxes2 (torch.Tensor): shape (n, 4) in <x1, y1, x2, y2> format or
empty. If aligned is ``True``, then m and n must be equal.
mode (str): "iou" (intersection over union) or iof (intersection over
foreground).
Returns:
torch.Tensor: Return the ious betweens boxes. If ``aligned`` is
``False``, the shape of ious is (m, n) else (m, 1).
Example:
>>> bboxes1 = torch.FloatTensor([
>>> [0, 0, 10, 10],
>>> [10, 10, 20, 20],
>>> [32, 32, 38, 42],
>>> ])
>>> bboxes2 = torch.FloatTensor([
>>> [0, 0, 10, 20],
>>> [0, 10, 10, 19],
>>> [10, 10, 20, 20],
>>> ])
>>> bbox_overlaps(bboxes1, bboxes2)
tensor([[0.5000, 0.0000, 0.0000],
[0.0000, 0.0000, 1.0000],
[0.0000, 0.0000, 0.0000]])
Example:
>>> empty = torch.FloatTensor([])
>>> nonempty = torch.FloatTensor([
>>> [0, 0, 10, 9],
>>> ])
>>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
>>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
>>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
"""
mode_dict = {'iou': 0, 'iof': 1}
assert mode in mode_dict.keys()
mode_flag = mode_dict[mode]
# Either the boxes are empty or the length of boxes' last dimension is 4
assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
assert offset == 1 or offset == 0
rows = bboxes1.size(0)
cols = bboxes2.size(0)
if aligned:
assert rows == cols
ious = bboxes1.new_zeros(rows)
else:
ious = bboxes1.new_zeros((rows, cols))
if rows * cols == 0:
return ious
if bboxes1.device.type == 'cpu' and torch.__version__ == 'parrots':
return _bbox_overlaps_cpu(
bboxes1, bboxes2, mode=mode, aligned=aligned, offset=offset)
ext_module.bbox_overlaps(
bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset)
return ious
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