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