def test_ap_score_2():
np.random.seed(1)
size = 100
np_y_pred = np.random.rand(size, 1)
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)
ap_metric = AveragePrecision()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
ap_metric.reset()
n_iters = 10
batch_size = size // n_iters
for i in range(n_iters):
idx = i * batch_size
ap_metric.update(
(y_pred[idx:idx + batch_size], y[idx:idx + batch_size]))
ap = ap_metric.compute()
assert ap == np_ap
def test_input_types():
ap = AveragePrecision()
ap.reset()
output1 = (torch.rand(4,
3), torch.randint(0,
2,
size=(4, 3),
dtype=torch.long))
ap.update(output1)
with pytest.raises(
ValueError,
match=
r"Incoherent types between input y_pred and stored predictions"):
ap.update((torch.randint(0, 5,
size=(4, 3)), torch.randint(0, 2,
size=(4, 3))))
with pytest.raises(
ValueError,
match=r"Incoherent types between input y and stored targets"):
ap.update((torch.rand(4,
3), torch.randint(0, 2,
size=(4, 3)).to(torch.int32)))
with pytest.raises(
ValueError,
match=
r"Incoherent types between input y_pred and stored predictions"):
ap.update((torch.randint(0, 2, size=(10, )).long(),
torch.randint(0, 2, size=(10, 5)).long()))
def _test(y_pred, y, n_iters, metric_device):
metric_device = torch.device(metric_device)
ap = AveragePrecision(device=metric_device)
torch.manual_seed(10 + rank)
ap.reset()
ap.update((y_pred, y))
if n_iters > 1:
batch_size = y.shape[0] // n_iters + 1
for i in range(n_iters):
idx = i * batch_size
ap.update(
(y_pred[idx:idx + batch_size], y[idx:idx + batch_size]))
# gather y_pred, y
y_pred = idist.all_gather(y_pred)
y = idist.all_gather(y)
np_y = y.cpu().numpy()
np_y_pred = y_pred.cpu().numpy()
res = ap.compute()
assert isinstance(res, float)
assert average_precision_score(np_y, np_y_pred) == pytest.approx(res)
def test_ap_score():
size = 100
np_y_pred = np.random.rand(size, 5)
np_y = np.random.randint(0, 2, size=(size, 5), dtype=np.long)
np_ap = average_precision_score(np_y, np_y_pred)
ap_metric = AveragePrecision()
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
ap_metric.reset()
ap_metric.update((y_pred, y))
ap = ap_metric.compute()
assert ap == np_ap
def test_ap_score_with_activation():
size = 100
np_y_pred = np.random.rand(size, 5)
np_y_pred_softmax = torch.softmax(torch.from_numpy(np_y_pred),
dim=1).numpy()
np_y = np.random.randint(0, 2, size=(size, 5), dtype=np.long)
np_ap = average_precision_score(np_y, np_y_pred_softmax)
ap_metric = AveragePrecision(activation=torch.nn.Softmax(dim=1))
y_pred = torch.from_numpy(np_y_pred)
y = torch.from_numpy(np_y)
ap_metric.reset()
ap_metric.update((y_pred, y))
ap = ap_metric.compute()
assert ap == np_ap