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(), ),
dtype=torch.double).to(device)
y_preds = torch.rand(size=(offset * idist.get_world_size(), ),
dtype=torch.double).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 = FractionalBias(device=metric_device)
m.attach(engine, "fb")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "fb" in engine.state.metrics
res = engine.state.metrics["fb"]
if isinstance(res, torch.Tensor):
res = res.cpu().numpy()
np_y_true = y_true.cpu().numpy()
np_y_preds = y_preds.cpu().numpy()
np_sum = (2 * (np_y_true - np_y_preds) /
(np_y_preds + np_y_true + 1e-30)).sum()
np_len = len(y_preds)
np_ans = np_sum / np_len
assert pytest.approx(res, rel=tol) == np_ans
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 = FractionalBias()
m.attach(engine, "fb")
np_y = y.double().numpy().ravel()
np_y_pred = y_pred.double().numpy().ravel()
data = list(range(y_pred.shape[0] // batch_size))
fb = engine.run(data, max_epochs=1).metrics["fb"]
np_sum = (2 * (np_y - np_y_pred) / (np_y_pred + np_y)).sum()
np_len = len(y_pred)
np_ans = np_sum / np_len
assert np_ans == pytest.approx(fb)
