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 = MaximumAbsoluteError(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()
np_y_preds = y_preds.cpu().numpy()
np_max = np.max(np.abs((np_y_preds - np_y_true)))
assert pytest.approx(res) == np_max
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 = MaximumAbsoluteError()
m.attach(engine, "mae")
np_y = y.numpy()
np_y_pred = y_pred.numpy()
data = list(range(y_pred.shape[0] // batch_size))
mae = engine.run(data, max_epochs=1).metrics["mae"]
np_max = np.max(np.abs((np_y_pred - np_y)))
assert np_max == pytest.approx(mae)