def test_wrong_input_shapes(): m = FractionalAbsoluteError() 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 = FractionalAbsoluteError() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (2 * np.abs((a - ground_truth)) / (np.abs(a) + np.abs(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 += (2 * np.abs((b - ground_truth)) / (np.abs(b) + np.abs(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 += (2 * np.abs((c - ground_truth)) / (np.abs(c) + np.abs(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 += (2 * np.abs((d - ground_truth)) / (np.abs(d) + np.abs(ground_truth))).sum() np_len += len(d) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans)
def test_wrong_input_shapes(): m = FractionalAbsoluteError() 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 = FractionalAbsoluteError(device=metric_device) torch.manual_seed(10 + rank) y_pred = torch.rand(size=(100,), device=device) y = torch.rand(size=(100,), device=device) m.update((y_pred, y)) # gather y_pred, y y_pred = idist.all_gather(y_pred) y = idist.all_gather(y) np_y = y.cpu().numpy() np_y_pred = y_pred.cpu().numpy() np_sum = (2 * np.abs((np_y_pred - np_y)) / (np.abs(np_y_pred) + np.abs(np_y))).sum() np_len = len(np_y_pred) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans)