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)
