def _test(metric_device):
with pytest.raises(NotComputableError):
v = GeometricAverage(device=metric_device)
v.compute()
decimal = 5 if device.type != "xla" else 4
mean_var = GeometricAverage(device=metric_device)
y_true = torch.rand(100, dtype=torch.float64) + torch.randint(0, 10, size=(100,)).double()
y_true = y_true.to(device)
for y in y_true:
mean_var.update(y)
m = mean_var.compute()
log_y_true = torch.log(y_true)
log_y_true = idist.all_reduce(log_y_true)
np.testing.assert_almost_equal(
m, torch.exp(log_y_true.mean(dim=0) / idist.get_world_size()).item(), decimal=decimal
)
mean_var = GeometricAverage(device=metric_device)
y_true = torch.rand(100, 10, dtype=torch.float64) + torch.randint(0, 10, size=(100, 10)).double()
y_true = y_true.to(device)
for y in y_true:
mean_var.update(y)
m = mean_var.compute()
log_y_true = torch.log(y_true)
log_y_true = idist.all_reduce(log_y_true)
np.testing.assert_almost_equal(
m.cpu().numpy(), torch.exp(log_y_true.mean(dim=0) / idist.get_world_size()).cpu().numpy(), decimal=decimal
)
def test_geom_average():
with pytest.raises(NotComputableError):
v = GeometricAverage()
v.compute()
mean_var = GeometricAverage()
y_true = torch.rand(100) + torch.randint(0, 10, size=(100, )).float()
for y in y_true:
mean_var.update(y.item())
m = mean_var.compute()
assert m.item() == pytest.approx(_geom_mean(y_true))
mean_var = GeometricAverage()
y_true = torch.rand(100, 10) + torch.randint(0, 10, size=(100, 10)).float()
for y in y_true:
mean_var.update(y)
m = mean_var.compute()
np.testing.assert_almost_equal(m.numpy(), _geom_mean(y_true), decimal=5)
mean_var = GeometricAverage()
y_true = torch.rand(8, 16, 10) + torch.randint(0, 10,
size=(8, 16, 10)).float()
for y in y_true:
mean_var.update(y)
m = mean_var.compute()
np.testing.assert_almost_equal(m.numpy(),
_geom_mean(y_true.reshape(-1, 10)),
decimal=5)
def _test_distrib_geom_average(device):
import torch.distributed as dist
with pytest.raises(NotComputableError):
v = GeometricAverage(device=device)
v.compute()
mean_var = GeometricAverage(device=device)
y_true = torch.rand(100, dtype=torch.float64) + torch.randint(0, 10, size=(100,)).double()
y_true = y_true.to(device)
for y in y_true:
mean_var.update(y)
m = mean_var.compute()
log_y_true = torch.log(y_true)
dist.all_reduce(log_y_true)
assert m.item() == pytest.approx(torch.exp(log_y_true.mean(dim=0) / dist.get_world_size()).item())
mean_var = GeometricAverage(device=device)
y_true = torch.rand(100, 10, dtype=torch.float64) + torch.randint(0, 10, size=(100, 10)).double()
y_true = y_true.to(device)
for y in y_true:
mean_var.update(y)
m = mean_var.compute()
log_y_true = torch.log(y_true)
dist.all_reduce(log_y_true)
np.testing.assert_almost_equal(
m.cpu().numpy(), torch.exp(log_y_true.mean(dim=0) / dist.get_world_size()).cpu().numpy(), decimal=5
)