def test_wrong_input_shapes(): m = GeometricMeanAbsoluteError() 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_wrong_input_shapes(): m = GeometricMeanAbsoluteError() 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 = GeometricMeanAbsoluteError(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() sum_errors = (np.log(np.abs(np_y - np_y_pred))).sum() np_len = len(y_pred) np_ans = np.exp(sum_errors / np_len) assert np_ans == pytest.approx(res)
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) np_prod = 1.0 m = GeometricMeanAbsoluteError() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - a) np_prod = np.multiply.reduce(errors) * np_prod np_len = len(a) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - b) np_prod = np.multiply.reduce(errors) * np_prod np_len += len(b) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - c) np_prod = np.multiply.reduce(errors) * np_prod np_len += len(c) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) errors = np.abs(ground_truth - d) np_prod = np.multiply.reduce(errors) * np_prod np_len += len(d) np_ans = np.power(np_prod, 1.0 / np_len) assert m.compute() == pytest.approx(np_ans)