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(),)).to(device) y_preds = torch.rand(size=(offset * idist.get_world_size(),)).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 = ManhattanDistance(device=metric_device) m.attach(engine, "md") data = list(range(n_iters)) engine.run(data=data, max_epochs=n_epochs) assert "md" in engine.state.metrics res = engine.state.metrics["md"] if isinstance(res, torch.Tensor): res = res.cpu().numpy() np_y_true = y_true.cpu().numpy() np_y_preds = y_preds.cpu().numpy() assert pytest.approx(res) == manhattan.pairwise([np_y_preds, np_y_true])[0][1]
def test_wrong_input_shapes(): m = ManhattanDistance() 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_wrong_input_shapes(): m = ManhattanDistance() 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 = ManhattanDistance(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() assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(res)
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 = ManhattanDistance() m.attach(engine, "md") np_y = y.numpy().ravel() np_y_pred = y_pred.numpy().ravel() manhattan = DistanceMetric.get_metric("manhattan") data = list(range(y_pred.shape[0] // batch_size)) md = engine.run(data, max_epochs=1).metrics["md"] assert manhattan.pairwise([np_y_pred, np_y])[0][1] == pytest.approx(md)
def test_mahattan_distance(): 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 = ManhattanDistance() manhattan = DistanceMetric.get_metric("manhattan") m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_sum = np.abs(ground_truth - a).sum() assert m.compute() == pytest.approx(np_sum) assert manhattan.pairwise([a, ground_truth])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_sum += np.abs(ground_truth - b).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([a, b]) v2 = np.hstack([ground_truth, ground_truth]) assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_sum += np.abs(ground_truth - c).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([v1, c]) v2 = np.hstack([v2, ground_truth]) assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_sum += np.abs(ground_truth - d).sum() assert m.compute() == pytest.approx(np_sum) v1 = np.hstack([v1, d]) v2 = np.hstack([v2, ground_truth]) assert manhattan.pairwise([v1, v2])[0][1] == pytest.approx(np_sum)
def test_error_is_not_nan(): m = ManhattanDistance() 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_mahattan_distance(): 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 = ManhattanDistance() m.update((torch.from_numpy(a), torch.from_numpy(ground_truth))) np_ans = (ground_truth - a).sum() assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(b), torch.from_numpy(ground_truth))) np_ans += (ground_truth - b).sum() assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(c), torch.from_numpy(ground_truth))) np_ans += (ground_truth - c).sum() assert m.compute() == pytest.approx(np_ans) m.update((torch.from_numpy(d), torch.from_numpy(ground_truth))) np_ans += (ground_truth - d).sum() assert m.compute() == pytest.approx(np_ans)