def test_integration_median_absolute_error_with_output_transform():
np.random.seed(1)
size = 105
np_y_pred = np.random.rand(size, 1)
np_y = np.random.rand(size, 1)
np.random.shuffle(np_y)
np_median_absolute_error = np.median(np.abs(np_y - np_y_pred))
batch_size = 15
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 idx, torch.from_numpy(y_pred_batch), torch.from_numpy(y_true_batch)
engine = Engine(update_fn)
m = MedianAbsoluteError(output_transform=lambda x: (x[1], x[2]))
m.attach(engine, "median_absolute_error")
data = list(range(size // batch_size))
median_absolute_error = engine.run(data, max_epochs=1).metrics[
"median_absolute_error"
]
assert np_median_absolute_error == pytest.approx(median_absolute_error)
def _test(n_epochs, metric_device):
metric_device = torch.device(metric_device)
n_iters = 80
size = 105
y_true = torch.rand(size=(size, )).to(device)
y_preds = torch.rand(size=(size, )).to(device)
def update(engine, i):
return (
y_preds[i * size:(i + 1) * size],
y_true[i * size:(i + 1) * size],
)
engine = Engine(update)
m = MedianAbsoluteError(device=metric_device)
m.attach(engine, "mae")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "mae" in engine.state.metrics
res = engine.state.metrics["mae"]
np_y_true = y_true.cpu().numpy().ravel()
np_y_preds = y_preds.cpu().numpy().ravel()
e = np.abs(np_y_true - np_y_preds)
np_res = np.median(e)
assert pytest.approx(res) == np_res
