def iou(
preds: Tensor,
target: Tensor,
ignore_index: Optional[int] = None,
absent_score: float = 0.0,
threshold: float = 0.5,
num_classes: Optional[int] = None,
reduction: str = 'elementwise_mean',
) -> Tensor:
r"""
Computes `Intersection over union, or Jaccard index calculation <https://en.wikipedia.org/wiki/Jaccard_index>`_:
.. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}
Where: :math:`A` and :math:`B` are both tensors of the same size,
containing integer class values. They may be subject to conversion from
input data (see description below).
Note that it is different from box IoU.
If preds and target are the same shape and preds is a float tensor, we use the ``self.threshold`` argument
to convert into integer labels. This is the case for binary and multi-label probabilities.
If pred has an extra dimension as in the case of multi-class scores we
perform an argmax on ``dim=1``.
Args:
preds: tensor containing predictions from model (probabilities, or labels) with shape ``[N, d1, d2, ...]``
target: tensor containing ground truth labels with shape ``[N, d1, d2, ...]``
ignore_index: optional int specifying a target class to ignore. If given,
this class index does not contribute to the returned score, regardless
of reduction method. Has no effect if given an int that is not in the
range [0, num_classes-1], where num_classes is either given or derived
from pred and target. By default, no index is ignored, and all classes are used.
absent_score: score to use for an individual class, if no instances of
the class index were present in `pred` AND no instances of the class
index were present in `target`. For example, if we have 3 classes,
[0, 0] for `pred`, and [0, 2] for `target`, then class 1 would be
assigned the `absent_score`.
threshold:
Threshold value for binary or multi-label probabilities. default: 0.5
num_classes:
Optionally specify the number of classes
reduction: a method to reduce metric score over labels.
- ``'elementwise_mean'``: takes the mean (default)
- ``'sum'``: takes the sum
- ``'none'``: no reduction will be applied
Return:
IoU score : Tensor containing single value if reduction is
'elementwise_mean', or number of classes if reduction is 'none'
Example:
>>> from torchmetrics.functional import iou
>>> target = torch.randint(0, 2, (10, 25, 25))
>>> pred = torch.tensor(target)
>>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
>>> iou(pred, target)
tensor(0.9660)
"""
num_classes = get_num_classes(preds=preds,
target=target,
num_classes=num_classes)
confmat = _confusion_matrix_update(preds, target, num_classes, threshold)
return _iou_from_confmat(confmat, num_classes, ignore_index, absent_score,
reduction)
def stat_scores_multiple_classes(
pred: torch.Tensor,
target: torch.Tensor,
num_classes: Optional[int] = None,
argmax_dim: int = 1,
reduction: str = 'none',
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
torch.Tensor]:
"""
.. deprecated::
Use :func:`torchmetrics.functional.stat_scores`. Will be removed in v1.4.0.
"""
rank_zero_deprecation(
"This `stat_scores_multiple_classes` was deprecated in v1.2.0 in favor of"
" `from pytorch_lightning.metrics.functional import stat_scores`."
" It will be removed in v1.4.0")
if pred.ndim == target.ndim + 1:
pred = to_categorical(pred, argmax_dim=argmax_dim)
num_classes = get_num_classes(pred=pred,
target=target,
num_classes=num_classes)
if pred.dtype != torch.bool:
pred = pred.clamp_max(max=num_classes)
if target.dtype != torch.bool:
target = target.clamp_max(max=num_classes)
possible_reductions = ('none', 'sum', 'elementwise_mean')
if reduction not in possible_reductions:
raise ValueError("reduction type %s not supported" % reduction)
if reduction == 'none':
pred = pred.view((-1, )).long()
target = target.view((-1, )).long()
tps = torch.zeros((num_classes + 1, ), device=pred.device)
fps = torch.zeros((num_classes + 1, ), device=pred.device)
fns = torch.zeros((num_classes + 1, ), device=pred.device)
sups = torch.zeros((num_classes + 1, ), device=pred.device)
match_true = (pred == target).float()
match_false = 1 - match_true
tps.scatter_add_(0, pred, match_true)
fps.scatter_add_(0, pred, match_false)
fns.scatter_add_(0, target, match_false)
tns = pred.size(0) - (tps + fps + fns)
sups.scatter_add_(0, target, torch.ones_like(match_true))
tps = tps[:num_classes]
fps = fps[:num_classes]
tns = tns[:num_classes]
fns = fns[:num_classes]
sups = sups[:num_classes]
elif reduction == 'sum' or reduction == 'elementwise_mean':
count_match_true = (pred == target).sum().float()
oob_tp, oob_fp, oob_tn, oob_fn, oob_sup = stat_scores(
pred, target, num_classes, argmax_dim)
tps = count_match_true - oob_tp
fps = pred.nelement() - count_match_true - oob_fp
fns = pred.nelement() - count_match_true - oob_fn
tns = pred.nelement() * (num_classes + 1) - (tps + fps + fns + oob_tn)
sups = pred.nelement() - oob_sup.float()
if reduction == 'elementwise_mean':
tps /= num_classes
fps /= num_classes
fns /= num_classes
tns /= num_classes
sups /= num_classes
return tps.float(), fps.float(), tns.float(), fns.float(), sups.float()
def test_get_num_classes(preds, target, num_classes, expected_num_classes):
assert get_num_classes(preds, target, num_classes) == expected_num_classes