def test_mean_error():
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 = MeanNormalizedBias()
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
np_sum = ((ground_truth - 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 += ((ground_truth - 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 += ((ground_truth - 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 += ((ground_truth - d) / ground_truth).sum()
np_len += len(d)
np_ans = np_sum / np_len
assert m.compute() == pytest.approx(np_ans)
def test_zero_sample():
m = MeanNormalizedBias()
with pytest.raises(
NotComputableError,
match=
r"MeanNormalizedBias must have at least one example before it can be computed"
):
m.compute()
def _test(metric_device):
metric_device = torch.device(metric_device)
m = MeanNormalizedBias(device=metric_device)
torch.manual_seed(10 + rank)
y_pred = torch.randint(1, 11, size=(10, ), device=device).float()
y = torch.randint(1, 11, 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 = ((np_y - np_y_pred) / np_y).sum()
np_len = len(np_y_pred)
np_ans = np_sum / np_len
assert np_ans == pytest.approx(res)
def test_zero_div():
m = MeanNormalizedBias()
with pytest.raises(NotComputableError):
m.compute()
