Bugfix for custom prefix/postfix and metric collection

CHANGELOG.md

@@ -55,6 +55,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed performance issues in `RecallAtFixedPrecision` for large batch sizes ([#2042](https://github.com/Lightning-AI/torchmetrics/pull/2042))
 
 
+- Fixed bug related to `MetricCollection` used with custom metrics have `prefix`/`postfix` attributes ([#2070](https://github.com/Lightning-AI/torchmetrics/pull/2070))
+
 ## [1.1.1] - 2023-08-29
 
 ### Added

src/torchmetrics/collections.py

@@ -339,17 +339,18 @@ def _compute_and_reduce(
         _, duplicates = _flatten_dict(result)
 
         flattened_results = {}
-        for k, res in result.items():
+        for k, m in self.items(keep_base=True, copy_state=False):
+            res = result[k]
             if isinstance(res, dict):
                 for key, v in res.items():
                     # if duplicates of keys we need to add unique prefix to each key
                     if duplicates:
                         stripped_k = k.replace(getattr(m, "prefix", ""), "")
                         stripped_k = stripped_k.replace(getattr(m, "postfix", ""), "")
                         key = f"{stripped_k}_{key}"
-                    if hasattr(m, "prefix") and m.prefix is not None:
+                    if getattr(m, "_from_collection", None) and m.prefix is not None:
                         key = f"{m.prefix}{key}"
-                    if hasattr(m, "postfix") and m.postfix is not None:
+                    if getattr(m, "_from_collection", None) and m.postfix is not None:
                         key = f"{key}{m.postfix}"
                     flattened_results[key] = v
             else:
@@ -425,6 +426,7 @@ def add_metrics(
                     for k, v in metric.items(keep_base=False):
                         v.postfix = metric.postfix
                         v.prefix = metric.prefix
+                        v._from_collection = True
                         self[f"{name}_{k}"] = v
         elif isinstance(metrics, Sequence):
             for metric in metrics:
@@ -442,6 +444,7 @@ def add_metrics(
                     for k, v in metric.items(keep_base=False):
                         v.postfix = metric.postfix
                         v.prefix = metric.prefix
+                        v._from_collection = True
                         self[k] = v
         else:
             raise ValueError(

tests/unittests/bases/test_collections.py

@@ -644,3 +644,36 @@ def test_double_nested_collections(base_metrics, expected):
 
     for key in val:
         assert key in expected
+
+
+def test_with_custom_prefix_postfix():
+    """Test that metric colection does not clash with custom prefix and postfix in users metrics.
+
+    See issue: https://github.com/Lightning-AI/torchmetrics/issues/2065
+
+    """
+
+    class CustomAccuracy(MulticlassAccuracy):
+        prefix = "my_prefix"
+        postfix = "my_postfix"
+
+        def compute(self):
+            value = super().compute()
+            return {f"{self.prefix}/accuracy/{self.postfix}": value}
+
+    class CustomPrecision(MulticlassAccuracy):
+        prefix = "my_prefix"
+        postfix = "my_postfix"
+
+        def compute(self):
+            value = super().compute()
+            return {f"{self.prefix}/precision/{self.postfix}": value}
+
+    metrics = MetricCollection([CustomAccuracy(num_classes=2), CustomPrecision(num_classes=2)])
+
+    # Update metrics with current batch
+    res = metrics(torch.tensor([1, 0, 0, 1]), torch.tensor([1, 0, 0, 0]))
+
+    # Print the calculated metrics
+    assert "my_prefix/accuracy/my_postfix" in res
+    assert "my_prefix/precision/my_postfix" in res