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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet3d.registry import MODELS
def test_uncertain_smooth_l1_loss():
from mmdet3d.models.losses import UncertainL1Loss, UncertainSmoothL1Loss
# reduction should be in ['none', 'mean', 'sum']
with pytest.raises(AssertionError):
uncertain_l1_loss = UncertainL1Loss(reduction='l2')
with pytest.raises(AssertionError):
uncertain_smooth_l1_loss = UncertainSmoothL1Loss(reduction='l2')
pred = torch.tensor([1.5783, 0.5972, 1.4821, 0.9488])
target = torch.tensor([1.0813, -0.3466, -1.1404, -0.9665])
sigma = torch.tensor([-1.0053, 0.4710, -1.7784, -0.8603])
# test uncertain l1 loss
uncertain_l1_loss_cfg = dict(
type='UncertainL1Loss', alpha=1.0, reduction='mean', loss_weight=1.0)
uncertain_l1_loss = MODELS.build(uncertain_l1_loss_cfg)
mean_l1_loss = uncertain_l1_loss(pred, target, sigma)
expected_l1_loss = torch.tensor(4.7069)
assert torch.allclose(mean_l1_loss, expected_l1_loss, atol=1e-4)
# test uncertain smooth l1 loss
uncertain_smooth_l1_loss_cfg = dict(
type='UncertainSmoothL1Loss',
alpha=1.0,
beta=0.5,
reduction='mean',
loss_weight=1.0)
uncertain_smooth_l1_loss = MODELS.build(uncertain_smooth_l1_loss_cfg)
mean_smooth_l1_loss = uncertain_smooth_l1_loss(pred, target, sigma)
expected_smooth_l1_loss = torch.tensor(3.9795)
assert torch.allclose(
mean_smooth_l1_loss, expected_smooth_l1_loss, atol=1e-4)
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