Fix prefix/postfix interaction between MetricCollection and ClasswiseWrapper

CHANGELOG.md

@@ -44,6 +44,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed bug related to expected input format of pycoco in `MeanAveragePrecision` ([#1913](https://github.com/Lightning-AI/torchmetrics/pull/1913))
 
 
+- Fixed bug related to the `prefix/postfix` arguments in `MetricCollection` and `ClasswiseWrapper` being duplicated ([#1918](https://github.com/Lightning-AI/torchmetrics/pull/1918))
+
 ## [1.0.0] - 2022-07-04
 
 ### Added

src/torchmetrics/wrappers/classwise.py

@@ -130,22 +130,22 @@ def __init__(
 
  if prefix is not None and not isinstance(prefix, str):
  raise ValueError(f"Expected argument `prefix` to either be `None` or a string but got {prefix}")
- self.prefix = prefix
+ self._prefix = prefix
 
  if postfix is not None and not isinstance(postfix, str):
  raise ValueError(f"Expected argument `postfix` to either be `None` or a string but got {postfix}")
- self.postfix = postfix
+ self._postfix = postfix
 
  self._update_count = 1
 
  def _convert(self, x: Tensor) -> Dict[str, Any]:
  # Will set the class name as prefix if neither prefix nor postfix is given
- if not self.prefix and not self.postfix:
+ if not self._prefix and not self._postfix:
  prefix = f"{self.metric.__class__.__name__.lower()}_"
  postfix = ""
  else:
- prefix = self.prefix or ""
- postfix = self.postfix or ""
+ prefix = self._prefix or ""
+ postfix = self._postfix or ""
  if self.labels is None:
  return {f"{prefix}{i}{postfix}": val for i, val in enumerate(x)}
  return {f"{prefix}{lab}{postfix}": val for lab, val in zip(self.labels, x)}

tests/unittests/wrappers/test_classwise.py

@@ -1,7 +1,7 @@
 import pytest
 import torch
 from torchmetrics import MetricCollection
-from torchmetrics.classification import MulticlassAccuracy, MulticlassRecall
+from torchmetrics.classification import MulticlassAccuracy, MulticlassF1Score, MulticlassRecall
 from torchmetrics.wrappers import ClasswiseWrapper
 
 
@@ -120,3 +120,33 @@ def _get_correct_name(base):
  assert name in val
  name = _get_correct_name(f"multiclassrecall_{lab}")
  assert name in val
+
+
+def test_double_use_of_prefix_with_metriccollection():
+ """Test that the expected output is produced when using prefix/postfix with metric collection.
+
+ See issue: https://github.com/Lightning-AI/torchmetrics/issues/1915
+
+ """
+ category_names = ["Tree", "Bush"]
+ num_classes = len(category_names)
+
+ input_ = torch.rand((5, 2, 3, 3))
+ target = torch.ones((5, 2, 3, 3)).long()
+
+ val_metrics = MetricCollection(
+ {
+ "accuracy": MulticlassAccuracy(num_classes=num_classes),
+ "f1": ClasswiseWrapper(
+ MulticlassF1Score(num_classes=num_classes, average="none"),
+ category_names,
+ prefix="f_score_",
+ ),
+ },
+ prefix="val/",
+ )
+
+ res = val_metrics(input_, target)
+ assert "val/accuracy" in res
+ assert "val/f_score_Tree" in res
+ assert "val/f_score_Bush" in res