def test_wrong_input_shapes(): m = FractionalBias() with pytest.raises( ValueError, match=r"Input data shapes should be the same, but given"): m.update((torch.rand(4, 1, 2), 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, 1, 2))) with pytest.raises( ValueError, match=r"Input data shapes should be the same, but given"): m.update(( torch.rand(4, 1, 2), torch.rand(4, ), )) with pytest.raises( ValueError, match=r"Input data shapes should be the same, but given"): m.update(( torch.rand(4, ), torch.rand(4, 1, 2), ))
def test_zero_sample(): m = FractionalBias() with pytest.raises( NotComputableError, match= r"FractionalBias must have at least one example before it can be computed" ): m.compute()
def test_wrong_input_shapes(): m = FractionalBias() 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(n_epochs, metric_device): metric_device = torch.device(metric_device) n_iters = 80 s = 16 n_classes = 2 offset = n_iters * s y_true = torch.rand(size=(offset * idist.get_world_size(), ), dtype=torch.double).to(device) y_preds = torch.rand(size=(offset * idist.get_world_size(), ), dtype=torch.double).to(device) def update(engine, i): return ( y_preds[i * s + rank * offset:(i + 1) * s + rank * offset], y_true[i * s + rank * offset:(i + 1) * s + rank * offset], ) engine = Engine(update) m = FractionalBias(device=metric_device) m.attach(engine, "fb") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "fb" in engine.state.metrics res = engine.state.metrics["fb"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() np_sum = (2 * (np_y_true - np_y_preds) / (np_y_preds + np_y_true + 1e-30)).sum() np_len = len(y_preds) np_ans = np_sum / np_len assert pytest.approx(res, rel=tol) == np_ans
def _test(y_pred, y, batch_size): def update_fn(engine, batch): idx = (engine.state.iteration - 1) * batch_size y_true_batch = np_y[idx : idx + batch_size] y_pred_batch = np_y_pred[idx : idx + batch_size] return torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch) engine = Engine(update_fn) m = FractionalBias() m.attach(engine, "fb") np_y = y.double().numpy().ravel() np_y_pred = y_pred.double().numpy().ravel() data = list(range(y_pred.shape[0] // batch_size)) fb = engine.run(data, max_epochs=1).metrics["fb"] np_sum = (2 * (np_y - np_y_pred) / (np_y_pred + np_y)).sum() np_len = len(y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(fb)
def _test(metric_device): metric_device = torch.device(metric_device) m = FractionalBias(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 = (2 * (np_y - np_y_pred) / (np_y_pred + np_y + 1e-30)).sum() np_len = len(y_pred) np_ans = np_sum / np_len assert np_ans == pytest.approx(res, rel=tol)
def test_fractional_bias(): 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 = FractionalBias() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = (2 * (ground_truth - a) / (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 += (2 * (ground_truth - b) / (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 += (2 * (ground_truth - c) / (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 += (2 * (ground_truth - d) / (d + ground_truth)).sum() np_len += len(d) np_ans = np_sum / np_len assert m.compute() == pytest.approx(np_ans)
def test_error_is_not_nan(): m = FractionalBias() 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
def test_zero_div(): m = FractionalBias() with pytest.raises(NotComputableError): m.compute()