# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch def test_chamfer_disrance(): from mmdet3d.models.losses import ChamferDistance, chamfer_distance with pytest.raises(AssertionError): # test invalid mode ChamferDistance(mode='smoothl1') # test invalid type of reduction ChamferDistance(mode='l2', reduction=None) self = ChamferDistance( mode='l2', reduction='sum', loss_src_weight=1.0, loss_dst_weight=1.0) source = torch.tensor([[[-0.9888, 0.9683, -0.8494], [-6.4536, 4.5146, 1.6861], [2.0482, 5.6936, -1.4701], [-0.5173, 5.6472, 2.1748], [-2.8010, 5.4423, -1.2158], [2.4018, 2.4389, -0.2403], [-2.8811, 3.8486, 1.4750], [-0.2031, 3.8969, -1.5245], [1.3827, 4.9295, 1.1537], [-2.6961, 2.2621, -1.0976]], [[0.3692, 1.8409, -1.4983], [1.9995, 6.3602, 0.1798], [-2.1317, 4.6011, -0.7028], [2.4158, 3.1482, 0.3169], [-0.5836, 3.6250, -1.2650], [-1.9862, 1.6182, -1.4901], [2.5992, 1.2847, -0.8471], [-0.3467, 5.3681, -1.4755], [-0.8576, 3.3400, -1.7399], [2.7447, 4.6349, 0.1994]]]) target = torch.tensor([[[-0.4758, 1.0094, -0.8645], [-0.3130, 0.8564, -0.9061], [-0.1560, 2.0394, -0.8936], [-0.3685, 1.6467, -0.8271], [-0.2740, 2.2212, -0.7980]], [[1.4856, 2.5299, -1.0047], [2.3262, 3.3065, -0.9475], [2.4593, 2.5870, -0.9423], [0.0000, 0.0000, 0.0000], [0.0000, 0.0000, 0.0000]]]) loss_source, loss_target, indices1, indices2 = self( source, target, return_indices=True) assert torch.allclose(loss_source, torch.tensor(219.5936)) assert torch.allclose(loss_target, torch.tensor(22.3705)) expected_inds1 = [[0, 4, 4, 4, 4, 2, 4, 4, 4, 3], [0, 1, 0, 1, 0, 4, 2, 0, 0, 1]] expected_inds2 = [[0, 4, 4, 4, 4, 2, 4, 4, 4, 3], [0, 1, 0, 1, 0, 3, 2, 0, 0, 1]] assert (torch.equal(indices1, indices1.new_tensor(expected_inds1)) or torch.equal(indices1, indices1.new_tensor(expected_inds2))) assert torch.equal(indices2, indices2.new_tensor([[0, 0, 0, 0, 0], [0, 3, 6, 0, 0]])) loss_source, loss_target, indices1, indices2 = chamfer_distance( source, target, reduction='sum') assert torch.allclose(loss_source, torch.tensor(219.5936)) assert torch.allclose(loss_target, torch.tensor(22.3705)) assert (torch.equal(indices1, indices1.new_tensor(expected_inds1)) or torch.equal(indices1, indices1.new_tensor(expected_inds2))) assert (indices2 == indices2.new_tensor([[0, 0, 0, 0, 0], [0, 3, 6, 0, 0]])).all()