def test_accumulator_detached():
mae = MeanAbsoluteError()
y_pred = torch.tensor([[2.0], [-2.0]], requires_grad=True)
y = torch.zeros(2)
mae.update((y_pred, y))
assert not mae._sum_of_absolute_errors.requires_grad
def test_compute():
mae = MeanAbsoluteError()
y_pred = torch.Tensor([[2.0], [-2.0]])
y = torch.zeros(2)
mae.update((y_pred, y))
assert mae.compute() == 2.0
mae.reset()
y_pred = torch.Tensor([[3.0], [-3.0]])
y = torch.zeros(2)
mae.update((y_pred, y))
assert mae.compute() == 3.0
def _test_distrib_accumulator_device(device):
metric_devices = [torch.device("cpu")]
if device.type != "xla":
metric_devices.append(idist.device())
for metric_device in metric_devices:
mae = MeanAbsoluteError(device=metric_device)
for dev in [mae._device, mae._sum_of_absolute_errors.device]:
assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"
y_pred = torch.tensor([[2.0], [-2.0]])
y = torch.zeros(2)
mae.update((y_pred, y))
for dev in [mae._device, mae._sum_of_absolute_errors.device]:
assert dev == metric_device, f"{type(dev)}:{dev} vs {type(metric_device)}:{metric_device}"
def _test_distrib_accumulator_device(device):
metric_devices = [torch.device("cpu")]
if device.type != "xla":
metric_devices.append(idist.device())
for metric_device in metric_devices:
mae = MeanAbsoluteError(device=metric_device)
assert mae._device == metric_device
assert mae._sum_of_absolute_errors.device == metric_device, "{}:{} vs {}:{}".format(
type(mae._sum_of_absolute_errors.device),
mae._sum_of_absolute_errors.device,
type(metric_device),
metric_device,
)
y_pred = torch.tensor([[2.0], [-2.0]])
y = torch.zeros(2)
mae.update((y_pred, y))
assert mae._sum_of_absolute_errors.device == metric_device, "{}:{} vs {}:{}".format(
type(mae._sum_of_absolute_errors.device),
mae._sum_of_absolute_errors.device,
type(metric_device),
metric_device,
)
