def compute(self) -> Tuple[float, float, float]:
"""
Compute metrics with accumulated statistics
Returns:
tuple of metrics: precision, recall, f1 score
"""
# ddp hotfix, could be done better
# but metric must handle DDP on it's own
if self._ddp_backend == "xla":
self.statistics = {
k: xm.mesh_reduce(k, v, np.sum)
for k, v in self.statistics.items()
}
elif self._ddp_backend == "ddp":
for key in self.statistics:
value: List[int] = all_gather(self.statistics[key])
value: int = sum(value)
self.statistics[key] = value
precision_value, recall_value, f1_value = get_binary_metrics(
tp=self.statistics["tp"],
fp=self.statistics["fp"],
fn=self.statistics["fn"],
zero_division=self.zero_division,
)
return precision_value, recall_value, f1_value
def update(self, outputs: torch.Tensor,
targets: torch.Tensor) -> Tuple[float, float, float]:
"""
Update statistics and return metrics intermediate results
Args:
outputs: predicted labels
targets: target labels
Returns:
tuple of intermediate metrics: precision, recall, f1 score
"""
tn, fp, fn, tp, support = super().update(outputs=outputs,
targets=targets)
precision_value, recall_value, f1_value = get_binary_metrics(
tp=tp, fp=fp, fn=fn, zero_division=self.zero_division)
return precision_value, recall_value, f1_value
def compute(self) -> Tuple[float, float, float]:
"""
Compute metrics with accumulated statistics
Returns:
tuple of metrics: precision, recall, f1 score
"""
# @TODO: ddp hotfix, could be done better
if self._is_ddp:
for key in self.statistics:
value: List[float] = all_gather(self.statistics[key])
value: float = sum(value)
self.statistics[key] = value
precision_value, recall_value, f1_value = get_binary_metrics(
tp=self.statistics["tp"],
fp=self.statistics["fp"],
fn=self.statistics["fn"],
zero_division=self.zero_division,
)
return precision_value, recall_value, f1_value