mirror of https://github.com/open-mmlab/mmcv.git
118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
|
|
class TestBoxIoURotated(object):
|
|
|
|
def test_box_iou_rotated_cpu(self):
|
|
from mmcv.ops import box_iou_rotated
|
|
np_boxes1 = np.asarray(
|
|
[[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6],
|
|
[7.0, 7.0, 8.0, 8.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_boxes2 = np.asarray(
|
|
[[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5],
|
|
[5.0, 5.0, 6.0, 7.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_expect_ious = np.asarray(
|
|
[[0.3708, 0.4351, 0.0000], [0.1104, 0.4487, 0.0424],
|
|
[0.0000, 0.0000, 0.3622]],
|
|
dtype=np.float32)
|
|
np_expect_ious_aligned = np.asarray([0.3708, 0.4487, 0.3622],
|
|
dtype=np.float32)
|
|
|
|
boxes1 = torch.from_numpy(np_boxes1)
|
|
boxes2 = torch.from_numpy(np_boxes2)
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2)
|
|
assert np.allclose(ious.cpu().numpy(), np_expect_ious, atol=1e-4)
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2, aligned=True)
|
|
assert np.allclose(
|
|
ious.cpu().numpy(), np_expect_ious_aligned, atol=1e-4)
|
|
|
|
@pytest.mark.skipif(
|
|
not torch.cuda.is_available(), reason='requires CUDA support')
|
|
def test_box_iou_rotated_cuda(self):
|
|
from mmcv.ops import box_iou_rotated
|
|
np_boxes1 = np.asarray(
|
|
[[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6],
|
|
[7.0, 7.0, 8.0, 8.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_boxes2 = np.asarray(
|
|
[[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5],
|
|
[5.0, 5.0, 6.0, 7.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_expect_ious = np.asarray(
|
|
[[0.3708, 0.4351, 0.0000], [0.1104, 0.4487, 0.0424],
|
|
[0.0000, 0.0000, 0.3622]],
|
|
dtype=np.float32)
|
|
np_expect_ious_aligned = np.asarray([0.3708, 0.4487, 0.3622],
|
|
dtype=np.float32)
|
|
|
|
boxes1 = torch.from_numpy(np_boxes1).cuda()
|
|
boxes2 = torch.from_numpy(np_boxes2).cuda()
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2)
|
|
assert np.allclose(ious.cpu().numpy(), np_expect_ious, atol=1e-4)
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2, aligned=True)
|
|
assert np.allclose(
|
|
ious.cpu().numpy(), np_expect_ious_aligned, atol=1e-4)
|
|
|
|
def test_box_iou_rotated_iof_cpu(self):
|
|
from mmcv.ops import box_iou_rotated
|
|
np_boxes1 = np.asarray(
|
|
[[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6],
|
|
[7.0, 7.0, 8.0, 8.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_boxes2 = np.asarray(
|
|
[[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5],
|
|
[5.0, 5.0, 6.0, 7.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_expect_ious = np.asarray(
|
|
[[0.4959, 0.5306, 0.0000], [0.1823, 0.5420, 0.1832],
|
|
[0.0000, 0.0000, 0.4404]],
|
|
dtype=np.float32)
|
|
np_expect_ious_aligned = np.asarray([0.4959, 0.5420, 0.4404],
|
|
dtype=np.float32)
|
|
|
|
boxes1 = torch.from_numpy(np_boxes1)
|
|
boxes2 = torch.from_numpy(np_boxes2)
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2, mode='iof')
|
|
assert np.allclose(ious.cpu().numpy(), np_expect_ious, atol=1e-4)
|
|
ious = box_iou_rotated(boxes1, boxes2, mode='iof', aligned=True)
|
|
assert np.allclose(
|
|
ious.cpu().numpy(), np_expect_ious_aligned, atol=1e-4)
|
|
|
|
@pytest.mark.skipif(
|
|
not torch.cuda.is_available(), reason='requires CUDA support')
|
|
def test_box_iou_rotated_iof_cuda(self):
|
|
from mmcv.ops import box_iou_rotated
|
|
np_boxes1 = np.asarray(
|
|
[[1.0, 1.0, 3.0, 4.0, 0.5], [2.0, 2.0, 3.0, 4.0, 0.6],
|
|
[7.0, 7.0, 8.0, 8.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_boxes2 = np.asarray(
|
|
[[0.0, 2.0, 2.0, 5.0, 0.3], [2.0, 1.0, 3.0, 3.0, 0.5],
|
|
[5.0, 5.0, 6.0, 7.0, 0.4]],
|
|
dtype=np.float32)
|
|
np_expect_ious = np.asarray(
|
|
[[0.4959, 0.5306, 0.0000], [0.1823, 0.5420, 0.1832],
|
|
[0.0000, 0.0000, 0.4404]],
|
|
dtype=np.float32)
|
|
np_expect_ious_aligned = np.asarray([0.4959, 0.5420, 0.4404],
|
|
dtype=np.float32)
|
|
|
|
boxes1 = torch.from_numpy(np_boxes1).cuda()
|
|
boxes2 = torch.from_numpy(np_boxes2).cuda()
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2, mode='iof')
|
|
assert np.allclose(ious.cpu().numpy(), np_expect_ious, atol=1e-4)
|
|
|
|
ious = box_iou_rotated(boxes1, boxes2, mode='iof', aligned=True)
|
|
assert np.allclose(
|
|
ious.cpu().numpy(), np_expect_ious_aligned, atol=1e-4)
|