def test_wrong_input_shapes():
m = ManhattanDistance()
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(metric_device):
metric_device = torch.device(metric_device)
m = ManhattanDistance(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()
assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(res)
def test_wrong_input_shapes():
m = ManhattanDistance()
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_mahattan_distance():
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 = ManhattanDistance()
manhattan = DistanceMetric.get_metric("manhattan")
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
np_sum = np.abs(ground_truth - a).sum()
assert m.compute() == pytest.approx(np_sum)
assert manhattan.pairwise([a, ground_truth])[0][1] == pytest.approx(np_sum)
m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
np_sum += np.abs(ground_truth - b).sum()
assert m.compute() == pytest.approx(np_sum)
v1 = np.hstack([a, b])
v2 = np.hstack([ground_truth, ground_truth])
assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
np_sum += np.abs(ground_truth - c).sum()
assert m.compute() == pytest.approx(np_sum)
v1 = np.hstack([v1, c])
v2 = np.hstack([v2, ground_truth])
assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
np_sum += np.abs(ground_truth - d).sum()
assert m.compute() == pytest.approx(np_sum)
v1 = np.hstack([v1, d])
v2 = np.hstack([v2, ground_truth])
assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
def test_mahattan_distance():
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 = ManhattanDistance()
m.update((torch.from_numpy(a), torch.from_numpy(ground_truth)))
np_ans = (ground_truth - a).sum()
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(b), torch.from_numpy(ground_truth)))
np_ans += (ground_truth - b).sum()
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(c), torch.from_numpy(ground_truth)))
np_ans += (ground_truth - c).sum()
assert m.compute() == pytest.approx(np_ans)
m.update((torch.from_numpy(d), torch.from_numpy(ground_truth)))
np_ans += (ground_truth - d).sum()
assert m.compute() == pytest.approx(np_ans)
def test_error_is_not_nan():
m = ManhattanDistance()
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
