def get_metric_name_value_pairs(metric: Metric, default_name: str, reset: bool = False) -> Iterable[Tuple[str, float]]:
"""
Return the metric as in `Metric.get_metric` but as an iterable of string-float pairs.
"""
value = metric.get_metric(reset)
if isinstance(value, collections.abc.Mapping):
for sub_name, sub_value in value.items():
if isinstance(sub_value, collections.abc.Iterable):
for i, sub_value_i in enumerate(sub_value):
yield f"{sub_name}_{i}", sub_value_i
else:
yield sub_name, sub_value
elif isinstance(value, collections.abc.Iterable):
for i, sub_value in enumerate(value): # type: ignore
yield f"{default_name}_{i}", sub_value # type: ignore
else:
yield default_name, value
def global_distributed_metric(
global_rank: int,
world_size: int,
gpu_id: Union[int, torch.device],
metric: Metric,
metric_kwargs: Dict[str, List[Any]],
desired_values: Dict[str, Any],
exact: Union[bool, Tuple[float, float]] = True,
number_of_runs: int = 1,
):
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 _ in range(number_of_runs):
metric(**kwargs)
metrics = metric.get_metric(False)
if not isinstance(metrics, Dict) and not isinstance(desired_values, Dict):
metrics = {"metric_value": metrics}
desired_values = {"metric_value": desired_values}
# Call `assertion_metrics_values` to check if the metrics have the desired values.
if isinstance(exact, bool):
if exact:
rtol = 0.0
atol = 0.0
else:
rtol = 0.0001
atol = 1e-05
else:
rtol = exact[0]
atol = exact[1]
assert_metrics_values(metrics, desired_values, rtol, atol) # type: ignore