def test_zero_div():
a = torch.tensor([2.0, -1.0, -1.0, 2.0])
ground_truth = torch.tensor([0.0, 0.5, 0.2, 1.0])
m = MeanAbsoluteRelativeError()
with raises(NotComputableError):
m.update((a, ground_truth))
def test_wrong_input_shapes():
m = MeanAbsoluteRelativeError()
with raises(ValueError):
m.update((torch.rand(4, 1, 2), torch.rand(4, 1)))
with raises(ValueError):
m.update((torch.rand(4, 1), torch.rand(4, 1, 2)))
with raises(ValueError):
m.update((torch.rand(4, 1, 2), torch.rand(4,)))
with raises(ValueError):
m.update((torch.rand(4,), torch.rand(4, 1, 2)))
def test_wrong_input_shapes():
m = MeanAbsoluteRelativeError()
with raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4), torch.rand(4, 1)))
with raises(ValueError, match=r"Input data shapes should be the same, but given"):
m.update((torch.rand(4, 1), torch.rand(4,)))
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 = MeanAbsoluteRelativeError(device=metric_device)
m.attach(engine, "mare")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "mare" in engine.state.metrics
mare = engine.state.metrics["mare"]
np_y_true = y_true.cpu().numpy()
np_y_preds = y_preds.cpu().numpy()
abs_error = np.sum(abs(np_y_true - np_y_preds) / abs(np_y_true))
num_samples = len(y_preds)
np_res = abs_error / num_samples
assert approx(mare) == np_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 = MeanAbsoluteRelativeError()
m.attach(engine, "mare")
np_y = y.numpy().ravel()
np_y_pred = y_pred.numpy().ravel()
data = list(range(y_pred.shape[0] // batch_size))
mare = engine.run(data, max_epochs=1).metrics["mare"]
abs_error = np.sum(abs(np_y - np_y_pred) / abs(np_y))
num_samples = len(y_pred)
res = abs_error / num_samples
assert res == approx(mare)
def _test(metric_device):
metric_device = torch.device(metric_device)
m = MeanAbsoluteRelativeError(device=metric_device)
torch.manual_seed(10 + rank)
y_pred = torch.randint(1, 11, size=(10,), device=device).float()
y = torch.randint(1, 11, 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()
abs_error = np.sum(abs(np_y - np_y_pred) / abs(np_y))
num_samples = len(y_pred)
np_res = abs_error / num_samples
assert np_res == approx(res)
def test_zero_sample():
m = MeanAbsoluteRelativeError()
with raises(NotComputableError):
m.compute()
def test_mean_absolute_relative_error():
a = torch.rand(4)
b = torch.rand(4)
c = torch.rand(4)
d = torch.rand(4)
ground_truth = torch.rand(4)
m = MeanAbsoluteRelativeError()
m.update((a, ground_truth))
abs_error_a = torch.sum(
torch.abs(ground_truth - a) / torch.abs(ground_truth))
num_samples_a = a.size()[0]
sum_error = abs_error_a
sum_samples = num_samples_a
MARE_a = sum_error / sum_samples
assert m.compute() == approx(MARE_a.item())
m.update((b, ground_truth))
abs_error_b = torch.sum(
torch.abs(ground_truth - b) / torch.abs(ground_truth))
num_samples_b = b.size()[0]
sum_error += abs_error_b
sum_samples += num_samples_b
MARE_b = sum_error / sum_samples
assert m.compute() == approx(MARE_b.item())
m.update((c, ground_truth))
abs_error_c = torch.sum(
torch.abs(ground_truth - c) / torch.abs(ground_truth))
num_samples_c = c.size()[0]
sum_error += abs_error_c
sum_samples += num_samples_c
MARE_c = sum_error / sum_samples
assert m.compute() == approx(MARE_c.item())
m.update((d, ground_truth))
abs_error_d = torch.sum(
torch.abs(ground_truth - d) / torch.abs(ground_truth))
num_samples_d = d.size()[0]
sum_error += abs_error_d
sum_samples += num_samples_d
MARE_d = sum_error / sum_samples
assert m.compute() == approx(MARE_d.item())
def test_zero_sample():
m = MeanAbsoluteRelativeError()
with raises(
NotComputableError,
match=r"MeanAbsoluteRelativeError must have at least one sample"):
m.compute()
