def do_metric_reduction(
f: torch.Tensor,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
):
"""
This function is to do the metric reduction for calculated metrics of each example's each class.
Args:
f: a tensor that contains the calculated metric scores per batch and
per class. The first two dims should be batch and class.
reduction: {``"none"``, ``"mean"``, ``"sum"``, ``"mean_batch"``, ``"sum_batch"``,
``"mean_channel"``, ``"sum_channel"``}
Define the mode to reduce computation result of 1 batch data. Defaults to ``"mean"``.
Raises:
ValueError: When ``reduction`` is not one of
["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].
"""
# some elements might be Nan (if ground truth y was missing (zeros))
# we need to account for it
nans = torch.isnan(f)
not_nans = (~nans).float()
f[nans] = 0
t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
reduction = MetricReduction(reduction)
if reduction == MetricReduction.MEAN:
# 2 steps, first, mean by channel (accounting for nans), then by batch
not_nans = not_nans.sum(dim=1)
f = torch.where(not_nans > 0,
f.sum(dim=1) / not_nans, t_zero) # channel average
not_nans = (not_nans > 0).float().sum(dim=0)
f = torch.where(not_nans > 0,
f.sum(dim=0) / not_nans, t_zero) # batch average
elif reduction == MetricReduction.SUM:
not_nans = not_nans.sum(dim=[0, 1])
f = torch.sum(f, dim=[0, 1]) # sum over the batch and channel dims
elif reduction == MetricReduction.MEAN_BATCH:
not_nans = not_nans.sum(dim=0)
f = torch.where(not_nans > 0,
f.sum(dim=0) / not_nans, t_zero) # batch average
elif reduction == MetricReduction.SUM_BATCH:
not_nans = not_nans.sum(dim=0)
f = f.sum(dim=0) # the batch sum
elif reduction == MetricReduction.MEAN_CHANNEL:
not_nans = not_nans.sum(dim=1)
f = torch.where(not_nans > 0,
f.sum(dim=1) / not_nans, t_zero) # channel average
elif reduction == MetricReduction.SUM_CHANNEL:
not_nans = not_nans.sum(dim=1)
f = f.sum(dim=1) # the channel sum
elif reduction != MetricReduction.NONE:
raise ValueError(
f"Unsupported reduction: {reduction}, available options are "
'["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].'
)
return f, not_nans
def __init__(
self,
include_background: bool = True,
to_onehot_y: bool = False,
mutually_exclusive: bool = False,
sigmoid: bool = False,
other_act: Optional[Callable] = None,
logit_thresh: float = 0.5,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
) -> None:
super().__init__()
if sigmoid and other_act is not None:
raise ValueError(
"Incompatible values: ``sigmoid=True`` and ``other_act is not None``."
)
self.include_background = include_background
self.to_onehot_y = to_onehot_y
self.mutually_exclusive = mutually_exclusive
self.sigmoid = sigmoid
self.other_act = other_act
self.logit_thresh = logit_thresh
self.reduction: MetricReduction = MetricReduction(reduction)
self.not_nans: Optional[
torch.Tensor] = None # keep track for valid elements in the batch
def __init__(
self,
include_background: bool = True,
to_onehot_y: bool = False,
mutually_exclusive: bool = False,
sigmoid: bool = False,
logit_thresh: float = 0.5,
reduction: Union[MetricReduction, str] = MetricReduction.MEAN,
):
super().__init__()
self.include_background = include_background
self.to_onehot_y = to_onehot_y
self.mutually_exclusive = mutually_exclusive
self.sigmoid = sigmoid
self.logit_thresh = logit_thresh
self.reduction: MetricReduction = MetricReduction(reduction)
self.not_nans = None # keep track for valid elements in the batch