def test_soft_jaccard_score():
input_good = torch.Tensor([1, 0, 1]).float()
input_bad = torch.Tensor([0, 0, 0]).float()
target = torch.Tensor([1, 0, 1])
eps = 1e-5
jaccard_good = F.soft_jaccard_score(input_good, target, smooth=eps)
assert float(jaccard_good) == pytest.approx(1.0, eps)
jaccard_bad = F.soft_jaccard_score(input_bad, target, smooth=eps)
assert float(jaccard_bad) == pytest.approx(0.0, eps)
def test_soft_jaccard_score_2(y_true, y_pred, expected, eps):
y_true = torch.tensor(y_true, dtype=torch.float32)
y_pred = torch.tensor(y_pred, dtype=torch.float32)
actual = F.soft_jaccard_score(y_pred, y_true, dims=[1], eps=eps)
actual = actual.mean()
assert float(actual) == pytest.approx(expected, eps)
