def test_ill_opts(self):
with self.assertRaisesRegex(ValueError, ""):
MaskedLoss(loss=[])
dice_loss = DiceLoss(include_background=True, sigmoid=True, smooth_nr=1e-5, smooth_dr=1e-5)
with self.assertRaisesRegex(ValueError, ""):
masked = MaskedLoss(loss=dice_loss)
masked(input=torch.zeros((3, 1, 2, 2)), target=torch.zeros((3, 1, 2, 2)), mask=torch.zeros((3, 3, 2, 2)))
with self.assertRaisesRegex(ValueError, ""):
masked = MaskedLoss(loss=dice_loss)
masked(input=torch.zeros((3, 3, 2, 2)), target=torch.zeros((3, 2, 2, 2)), mask=torch.zeros((3, 3, 2, 2)))
def test_script(self):
input_param, expected_val = TEST_CASES[0]
size = [3, 3, 5, 5]
label = torch.randint(low=0, high=2, size=size)
label = torch.argmax(label, dim=1, keepdim=True)
pred = torch.randn(size)
loss = MaskedLoss(**input_param)
test_script_save(loss, pred, label)
def test_shape(self, input_param, expected_val):
size = [3, 3, 5, 5]
label = torch.randint(low=0, high=2, size=size)
label = torch.argmax(label, dim=1, keepdim=True)
pred = torch.randn(size)
result = MaskedLoss(**input_param)(pred, label, None)
out = result.detach().cpu().numpy()
checked = np.allclose(out, expected_val[0][0]) or np.allclose(out, expected_val[0][1])
self.assertTrue(checked)
mask = torch.randint(low=0, high=2, size=label.shape)
result = MaskedLoss(**input_param)(pred, label, mask)
out = result.detach().cpu().numpy()
checked = np.allclose(out, expected_val[1][0]) or np.allclose(out, expected_val[1][1])
self.assertTrue(checked)
def __init__(self, *args, **kwargs) -> None:
"""
Args follow :py:class:`monai.losses.DiceLoss`.
"""
super().__init__(*args, **kwargs)
self.spatial_weighted = MaskedLoss(loss=super().forward)