def test_misconfig(self, input_params):
with self.assertRaisesRegex(ValueError, 'compatib'):
dice_metric = MeanDice(**input_params)
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.ones((2, 1))
dice_metric.update([y_pred, y])
def test_shape_mismatch(self, input_params, _expected):
dice_metric = MeanDice(**input_params)
with self.assertRaises((AssertionError, ValueError)):
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.ones((2, 3))
dice_metric.update([y_pred, y])
with self.assertRaises((AssertionError, ValueError)):
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.ones((3, 2))
dice_metric.update([y_pred, y])
def test_compute(self, input_params, expected_avg):
dice_metric = MeanDice(**input_params)
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.ones((2, 1))
dice_metric.update([y_pred, y])
y_pred = torch.Tensor([[0, 1], [1, 0]])
y = torch.Tensor([[1.], [0.]])
dice_metric.update([y_pred, y])
avg_dice = dice_metric.compute()
self.assertAlmostEqual(avg_dice, expected_avg)