def test_wrong_input_shapes(): m = WaveHedgesDistance() 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_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) m = WaveHedgesDistance() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (np.abs(ground_truth - a) / np.maximum.reduce([a, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - b) / np.maximum.reduce([b, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - c) / np.maximum.reduce([c, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += (np.abs(ground_truth - d) / np.maximum.reduce([d, ground_truth])).sum() assert m.compute() == pytest.approx(np_sum)
def test_wrong_input_shapes(): m = WaveHedgesDistance() 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 = WaveHedgesDistance(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 = (np.abs(np_y - np_y_pred) / (np.maximum.reduce([np_y_pred, np_y]) + 1e-30)).sum() assert np_sum == pytest.approx(res)