def test_mean_absolute_error_computation(self, device: str):
mae = MeanAbsoluteError()
predictions = torch.tensor(
[[1.0, 1.5, 1.0], [2.0, 3.0, 3.5], [4.0, 5.0, 5.5], [6.0, 7.0, 7.5]], device=device
)
targets = torch.tensor(
[[0.0, 1.0, 0.0], [2.0, 2.0, 0.0], [4.0, 5.0, 0.0], [7.0, 7.0, 0.0]], device=device
)
mae(predictions, targets)
assert mae.get_metric() == 21.0 / 12.0
mask = torch.tensor(
[[True, True, False], [True, True, False], [True, True, False], [True, True, False]],
device=device,
)
mae(predictions, targets, mask)
assert mae.get_metric() == (21.0 + 3.5) / (12.0 + 8.0)
new_targets = torch.tensor(
[[2.0, 2.0, 0.0], [0.0, 1.0, 0.0], [7.0, 7.0, 0.0], [4.0, 5.0, 0.0]], device=device
)
mae(predictions, new_targets)
assert mae.get_metric() == (21.0 + 3.5 + 32.0) / (12.0 + 8.0 + 12.0)
mae.reset()
mae(predictions, new_targets)
assert mae.get_metric() == 32.0 / 12.0
def test_mean_absolute_error_computation(self):
mae = MeanAbsoluteError()
predictions = torch.Tensor([[1.0, 1.5, 1.0],
[2.0, 3.0, 3.5],
[4.0, 5.0, 5.5],
[6.0, 7.0, 7.5]])
targets = torch.Tensor([[0.0, 1.0, 0.0],
[2.0, 2.0, 0.0],
[4.0, 5.0, 0.0],
[7.0, 7.0, 0.0]])
mae(predictions, targets)
assert mae.get_metric() == 21.0 / 12.0
mask = torch.Tensor([[1.0, 1.0, 0.0],
[1.0, 1.0, 0.0],
[1.0, 1.0, 0.0],
[1.0, 1.0, 0.0]])
mae(predictions, targets, mask)
assert mae.get_metric() == (21.0 + 3.5) / (12.0 + 8.0)
new_targets = torch.Tensor([[2.0, 2.0, 0.0],
[0.0, 1.0, 0.0],
[7.0, 7.0, 0.0],
[4.0, 5.0, 0.0]])
mae(predictions, new_targets)
assert mae.get_metric() == (21.0 + 3.5 + 32.0) / (12.0 + 8.0 + 12.0)
mae.reset()
mae(predictions, new_targets)
assert mae.get_metric() == 32.0 / 12.0
def multiple_runs(
global_rank: int,
world_size: int,
gpu_id: Union[int, torch.device],
metric: MeanAbsoluteError,
metric_kwargs: Dict[str, List[Any]],
desired_values: Dict[str, Any],
exact: Union[bool, Tuple[float, float]] = True,
):
kwargs = {}
# Use the arguments meant for the process with rank `global_rank`.
for argname in metric_kwargs:
kwargs[argname] = metric_kwargs[argname][global_rank]
for i in range(200):
metric(**kwargs)
assert desired_values["mae"] == metric.get_metric()["mae"]