fix: clarify threshold error message

segmentation_models_pytorch/metrics/functional.py

@@ -120,7 +120,7 @@ def get_stats(
 
     if torch.is_floating_point(output) and threshold is None:
         raise ValueError(
-            f"Output should be one of the integer types if ``threshold`` is not None, got {output.dtype}."
+            f"Output should be one of the integer types if ``threshold`` is None, got {output.dtype}."
         )
 
     if torch.is_floating_point(output) and mode == "multiclass":

tests/test_metrics.py

@@ -0,0 +1,17 @@
+import pytest
+import torch
+
+from segmentation_models_pytorch.metrics import get_stats
+
+
+def test_get_stats_explains_float_output_requires_threshold() -> None:
+    output = torch.tensor([[[0.1, 0.8], [0.3, 0.7]]], dtype=torch.float32)
+    target = torch.tensor([[[0, 1], [0, 1]]], dtype=torch.long)
+
+    with pytest.raises(ValueError) as error:
+        get_stats(output, target, mode="binary", threshold=None)
+
+    assert str(error.value) == (
+        "Output should be one of the integer types if ``threshold`` is None, "
+        "got torch.float32."
+    )