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()