def test_ill_opts(self):
with self.assertRaisesRegex(ValueError, ""):
MaskedDiceLoss(sigmoid=True, softmax=True)
chn_input = torch.ones((1, 1, 3))
chn_target = torch.ones((1, 1, 3))
with self.assertRaisesRegex(ValueError, ""):
MaskedDiceLoss(reduction="unknown")(chn_input, chn_target)
with self.assertRaisesRegex(ValueError, ""):
MaskedDiceLoss(reduction=None)(chn_input, chn_target)
def test_input_warnings(self):
chn_input = torch.ones((1, 1, 3))
chn_target = torch.ones((1, 1, 3))
with self.assertWarns(Warning):
loss = MaskedDiceLoss(include_background=False)
loss.forward(chn_input, chn_target)
with self.assertWarns(Warning):
loss = MaskedDiceLoss(softmax=True)
loss.forward(chn_input, chn_target)
with self.assertWarns(Warning):
loss = MaskedDiceLoss(to_onehot_y=True)
loss.forward(chn_input, chn_target)
def test_ill_shape(self):
loss = MaskedDiceLoss()
with self.assertRaisesRegex(AssertionError, ""):
loss.forward(torch.ones((1, 2, 3)), torch.ones((4, 5, 6)))
def test_shape(self, input_param, input_data, expected_val):
result = MaskedDiceLoss(**input_param).forward(**input_data)
np.testing.assert_allclose(result.detach().cpu().numpy(), expected_val, rtol=1e-5)