Fix class_labels mutation across multi-metric write_metrics_reports (#8902)

### Description

`write_metrics_reports` rebinds and appends to its `class_labels`
parameter inside the `metric_details` loop. After the first metric's CSV
is written, `class_labels` is no longer `None`. It holds `["class0",
..., "classN", "mean"]`. On every subsequent metric the `else` branch
runs and appends another `"mean"`, producing headers like
`class0,class1,mean,mean,mean`.

This hits any evaluation with two or more metrics in `metric_details`,
for example Dice + IoU + Hausdorff. The only real caller `MetricsSaver`
always passes `class_labels=None`. The first metric's CSV is correct,
but every one after is structurally corrupt with mismatched header and
data columns.

The fix uses a local `labels` variable per iteration so the original
`class_labels` parameter is never modified. The existing test only
verified existence of subsequent metric files, not their headers. A
regression test is included.

### Types of changes
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.

---------

Signed-off-by: Soumya Snigdha Kundu <soumya_snigdha.kundu@kcl.ac.uk>
Co-authored-by: Eric Kerfoot <17726042+ericspod@users.noreply.github.com>

Author: aymuos15

monai/handlers/utils.py

@@ -122,15 +122,15 @@ class    mean    median    max    5percentile 95percentile  notnans
 
             # add the average value of all classes to v
             if class_labels is None:
-                class_labels = ["class" + str(i) for i in range(v.shape[1])]
+                labels = ["class" + str(i) for i in range(v.shape[1])]
             else:
-                class_labels = [str(i) for i in class_labels]  # ensure to have a list of str
+                labels = [str(i) for i in class_labels]  # ensure to have a list of str
 
-            class_labels += ["mean"]
+            labels += ["mean"]
             v = np.concatenate([v, np.nanmean(v, axis=1, keepdims=True)], axis=1)
 
             with open(os.path.join(save_dir, f"{k}_raw.csv"), "w") as f:
-                f.write(f"filename{deli}{deli.join(class_labels)}\n")
+                f.write(f"filename{deli}{deli.join(labels)}\n")
                 for i, b in enumerate(v):
                     f.write(
                         f"{images[i] if images is not None else str(i)}{deli}"
@@ -164,7 +164,7 @@ def _compute_op(op: str, d: np.ndarray) -> Any:
                 with open(os.path.join(save_dir, f"{k}_summary.csv"), "w") as f:
                     f.write(f"class{deli}{deli.join(ops)}\n")
                     for i, c in enumerate(np.transpose(v)):
-                        f.write(f"{class_labels[i]}{deli}{deli.join([f'{_compute_op(k, c):.4f}' for k in ops])}\n")
+                        f.write(f"{labels[i]}{deli}{deli.join([f'{_compute_op(k, c):.4f}' for k in ops])}\n")
 
 
 def from_engine(keys: KeysCollection, first: bool = False) -> Callable:

tests/handlers/test_write_metrics_reports.py

@@ -63,6 +63,28 @@ def test_content(self):
             self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_raw.csv")))
             self.assertTrue(os.path.exists(os.path.join(tempdir, "metric4_summary.csv")))
 
+    def test_multi_metric_details_headers(self):
+        with tempfile.TemporaryDirectory() as tempdir:
+            write_metrics_reports(
+                save_dir=Path(tempdir),
+                images=["img1", "img2"],
+                metrics=None,
+                metric_details={
+                    "m1": torch.tensor([[1, 2, 3], [4, 5, 6]]),
+                    "m2": torch.tensor([[7, 8], [9, 10]]),
+                    "m3": torch.tensor([[11, 12, 13, 14], [15, 16, 17, 18]]),
+                },
+                summary_ops=None,
+                deli=",",
+                output_type="csv",
+            )
+            for name, nclass in [("m1", 3), ("m2", 2), ("m3", 4)]:
+                path = os.path.join(tempdir, f"{name}_raw.csv")
+                self.assertTrue(os.path.exists(path))
+                with open(path) as f:
+                    header = f.readline().strip().split(",")
+                self.assertEqual(header, ["filename"] + [f"class{i}" for i in range(nclass)] + ["mean"])
+
 
 if __name__ == "__main__":
     unittest.main()