def test_wrong_input_shapes():
m = FractionalBias()
with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4), torch.rand(4, 1)))
with pytest.raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4, 1), torch.rand(4,)))
def test_fractional_bias():
a = np.random.randn(4)
b = np.random.randn(4)
c = np.random.randn(4)
d = np.random.randn(4)
ground_truth = np.random.randn(4)
m = FractionalBias()
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
np_sum = (2 * (ground_truth - a) / (a + ground_truth)).sum()
np_len = len(a)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
np_sum += (2 * (ground_truth - b) / (b + ground_truth)).sum()
np_len += len(b)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
np_sum += (2 * (ground_truth - c) / (c + ground_truth)).sum()
np_len += len(c)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
np_sum += (2 * (ground_truth - d) / (d + ground_truth)).sum()
np_len += len(d)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
def test_wrong_input_shapes():
m = FractionalBias()
with pytest.raises(ValueError):
m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))
with pytest.raises(ValueError):
m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))
with pytest.raises(ValueError):
m.update((torch.rand(4, 1, 2), torch.rand(4,)))
with pytest.raises(ValueError):
m.update((torch.rand(4,), torch.rand(4, 1, 2)))
def _test(metric_device):
metric_device = torch.device(metric_device)
m = FractionalBias(device=metric_device)
torch.manual_seed(10 + rank)
y_pred = torch.randint(0, 10, size=(10,), device=device).float()
y = torch.randint(0, 10, size=(10,), device=device).float()
m.update((y_pred, y))
# gather y_pred, y
y_pred = idist.all_gather(y_pred)
y = idist.all_gather(y)
np_y_pred = y_pred.cpu().numpy()
np_y = y.cpu().numpy()
res = m.compute()
np_sum = (2 * (np_y - np_y_pred) / (np_y_pred + np_y + 1e-30)).sum()
np_len = len(y_pred)
np_ans = np_sum / np_len
assert np_ans == pytest.approx(res, rel=tol)
def test_error_is_not_nan():
m = FractionalBias()
m.update((torch.zeros(4), torch.zeros(4)))
assert not (torch.isnan(m._sum_of_errors).any() or torch.isinf(m._sum_of_errors).any()), m._sum_of_errors
