def test_integration_ap_score_with_activated_output_transform():
np.random.seed(1)
size = 100
np_y_pred = np.random.rand(size, 1)
np_y_pred_softmax = torch.softmax(torch.from_numpy(np_y_pred),
dim=1).numpy()
np_y = np.zeros((size, ), dtype=np.long)
np_y[size // 2:] = 1
np.random.shuffle(np_y)
np_ap = average_precision_score(np_y, np_y_pred_softmax)
batch_size = 10
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)
ap_metric = AveragePrecision(
output_transform=lambda x: (torch.softmax(x[1], dim=1), x[2]))
ap_metric.attach(engine, 'ap')
data = list(range(size // batch_size))
ap = engine.run(data, max_epochs=1).metrics['ap']
assert ap == np_ap
def _test(y_preds, y_true, n_epochs, metric_device, update_fn):
metric_device = torch.device(metric_device)
engine = Engine(update_fn)
ap = AveragePrecision(device=metric_device)
ap.attach(engine, "ap")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "ap" in engine.state.metrics
res = engine.state.metrics["ap"]
true_res = average_precision_score(y_true.cpu().numpy(),
y_preds.cpu().numpy())
assert pytest.approx(res) == true_res
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.randint(0,
n_classes,
size=(offset * idist.get_world_size(),
10)).to(device)
y_preds = torch.randint(0,
n_classes,
size=(offset * idist.get_world_size(),
10)).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)
ap = AveragePrecision(device=metric_device)
ap.attach(engine, "ap")
data = list(range(n_iters))
engine.run(data=data, max_epochs=n_epochs)
assert "ap" in engine.state.metrics
res = engine.state.metrics["ap"]
if isinstance(res, torch.Tensor):
res = res.cpu().numpy()
true_res = average_precision_score(y_true.cpu().numpy(),
y_preds.cpu().numpy())
assert pytest.approx(res) == true_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 idx, torch.from_numpy(y_pred_batch), torch.from_numpy(
y_true_batch)
engine = Engine(update_fn)
ap_metric = AveragePrecision(output_transform=lambda x: (x[1], x[2]))
ap_metric.attach(engine, "ap")
np_y = y.numpy()
np_y_pred = y_pred.numpy()
np_ap = average_precision_score(np_y, np_y_pred)
data = list(range(y_pred.shape[0] // batch_size))
ap = engine.run(data, max_epochs=1).metrics["ap"]
assert isinstance(ap, float)
assert np_ap == pytest.approx(ap)