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import numpy as np |
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import torch |
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from mmdet3d.models.losses import RotatedIoU3DLoss |
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def test_rotated_iou_3d_loss(): |
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if not torch.cuda.is_available(): |
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return |
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boxes1 = torch.tensor([[.5, .5, .5, 1., 1., 1., .0], |
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[.5, .5, .5, 1., 1., 1., .0], |
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[.5, .5, .5, 1., 1., 1., .0], |
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[.5, .5, .5, 1., 1., 1., .0], |
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[.5, .5, .5, 1., 1., 1., .0]]).cuda() |
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boxes2 = torch.tensor([[.5, .5, .5, 1., 1., 1., .0], |
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[.5, .5, .5, 1., 1., 2., np.pi / 2], |
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[.5, .5, .5, 1., 1., 1., np.pi / 4], |
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[1., 1., 1., 1., 1., 1., .0], |
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[-1.5, -1.5, -1.5, 2.5, 2.5, 2.5, .0]]).cuda() |
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expect_ious = 1 - torch.tensor([[1., .5, .7071, 1 / 15, .0]]).cuda() |
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ious = RotatedIoU3DLoss(reduction='none')(boxes1, boxes2) |
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assert torch.allclose(ious, expect_ious, atol=1e-4) |
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