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Fix class_labels mutation across multi-metric write_metrics_reports #8902

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Fix class_labels mutation across multi-metric write_metrics_reports #8902

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41e587c
Fix class_labels mutation across multi-metric write_metrics_reports
aymuos15 Jun 7, 2026
e609d0a
Add regression test for multi-metric class_labels mutation
aymuos15 Jun 7, 2026
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10 changes: 5 additions & 5 deletions monai/handlers/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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}"
Expand Down Expand Up @@ -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:
Expand Down
22 changes: 22 additions & 0 deletions tests/handlers/test_write_metrics_reports.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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()
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