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