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# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmcv.ops import convex_giou, convex_iou
np_pointsets = np.asarray([[
1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 2.0, 1.0, 1.0, 3.0, 3.0, 1.0, 2.0, 3.0, 3.0,
2.0, 1.5, 1.5
],
[
1.5, 1.5, 2.5, 2.5, 1.5, 2.5, 2.5, 1.5, 1.5,
3.5, 3.5, 1.5, 2.5, 3.5, 3.5, 2.5, 2.0, 2.0
]])
np_polygons = np.asarray([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 1.0],
[1.0, 1.0, 1.0, 3.0, 3.0, 3.0, 3.0, 1.0]])
np_expected_iou = np.asarray([[0.2857, 0.8750], [0.0588, 0.4286]])
np_expected_giou = np.asarray([0.2857, 0.3831])
np_expected_grad = np.asarray([[
0.0204, 0.0408, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0612,
-0.0408, -0.0408, 0.0816, -0.0408, -0.0816, -0.0816, -0.0408, 0.0000,
0.0000
],
[
-0.1848, -0.1848, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, -0.1076, -0.0801,
-0.0801, -0.1076, -0.0367, -0.0734, -0.0734,
-0.0367, 0.0000, 0.0000
]])
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_convex_iou():
pointsets = torch.from_numpy(np_pointsets).cuda().float()
polygons = torch.from_numpy(np_polygons).cuda().float()
expected_iou = torch.from_numpy(np_expected_iou).cuda().float()
assert torch.allclose(
convex_iou(pointsets, polygons), expected_iou, atol=1e-3)
@pytest.mark.skipif(
not torch.cuda.is_available(), reason='requires CUDA support')
def test_convex_giou():
pointsets = torch.from_numpy(np_pointsets).cuda().float()
polygons = torch.from_numpy(np_polygons).cuda().float()
expected_giou = torch.from_numpy(np_expected_giou).cuda().float()
expected_grad = torch.from_numpy(np_expected_grad).cuda().float()
giou, grad = convex_giou(pointsets, polygons)
assert torch.allclose(giou, expected_giou, atol=1e-3)
assert torch.allclose(grad, expected_grad, atol=1e-3)
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