def test_wrong_input_shapes():
m = GeometricMeanAbsoluteError()
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_wrong_input_shapes():
m = GeometricMeanAbsoluteError()
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 = GeometricMeanAbsoluteError(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()
sum_errors = (np.log(np.abs(np_y - np_y_pred))).sum()
np_len = len(y_pred)
np_ans = np.exp(sum_errors / np_len)
assert np_ans == pytest.approx(res)
def test_compute():
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)
np_prod = 1.0
m = GeometricMeanAbsoluteError()
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
errors = np.abs(ground_truth - a)
np_prod = np.multiply.reduce(errors) * np_prod
np_len = len(a)
np_ans = np.power(np_prod, 1.0 / np_len)
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
errors = np.abs(ground_truth - b)
np_prod = np.multiply.reduce(errors) * np_prod
np_len += len(b)
np_ans = np.power(np_prod, 1.0 / np_len)
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
errors = np.abs(ground_truth - c)
np_prod = np.multiply.reduce(errors) * np_prod
np_len += len(c)
np_ans = np.power(np_prod, 1.0 / np_len)
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
errors = np.abs(ground_truth - d)
np_prod = np.multiply.reduce(errors) * np_prod
np_len += len(d)
np_ans = np.power(np_prod, 1.0 / np_len)
assert m.compute() == pytest.approx(np_ans)