Merge branch 'master' into plot/detection

CHANGELOG.md

@@ -104,6 +104,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed evaluation of `R2Score` with near constant target ([#1576](https://github.com/Lightning-AI/metrics/pull/1576))
 
 
+- Fixed bug related to `top_k>1` and `ignore_index!=None` in `StatScores` based metrics ([#1589](https://github.com/Lightning-AI/metrics/pull/1589))
+
+
 - Fixed corner case for `PearsonCorrCoef` when running in ddp mode but only on single device ([#1587](https://github.com/Lightning-AI/metrics/pull/1587))
 
 

src/torchmetrics/functional/classification/stat_scores.py

@@ -358,8 +358,9 @@ def _multiclass_stat_scores_update(
             preds = preds.clone()
             target = target.clone()
             idx = target == ignore_index
-            preds[idx] = num_classes
             target[idx] = num_classes
+            idx = idx.unsqueeze(1).repeat(1, num_classes, 1) if preds.ndim > target.ndim else idx
+            preds[idx] = num_classes
 
         if top_k > 1:
             preds_oh = torch.movedim(select_topk(preds, topk=top_k, dim=1), 1, -1)
@@ -374,7 +375,7 @@ def _multiclass_stat_scores_update(
             if 0 <= ignore_index <= num_classes - 1:
                 target_oh[target == ignore_index, :] = -1
             else:
-                preds_oh = preds_oh[..., :-1]
+                preds_oh = preds_oh[..., :-1] if top_k == 1 else preds_oh
                 target_oh = target_oh[..., :-1]
                 target_oh[target == num_classes, :] = -1
         sum_dim = [0, 1] if multidim_average == "global" else [1]

tests/unittests/classification/test_stat_scores.py

@@ -343,6 +343,21 @@ def test_top_k_multiclass(k, preds, target, average, expected):
     )
 
 
+def test_top_k_ignore_index_multiclass():
+    """Test that top_k argument works together with ignore_index."""
+    preds_without = torch.randn(10, 3).softmax(dim=-1)
+    target_without = torch.randint(3, (10,))
+    preds_with = torch.cat([preds_without, torch.randn(10, 3).softmax(dim=-1)], 0)
+    target_with = torch.cat([target_without, -100 * torch.ones(10)], 0).long()
+
+    res_without = multiclass_stat_scores(preds_without, target_without, num_classes=3, average="micro", top_k=2)
+    res_with = multiclass_stat_scores(
+        preds_with, target_with, num_classes=3, average="micro", top_k=2, ignore_index=-100
+    )
+
+    assert torch.allclose(res_without, res_with)
+
+
 def test_multiclass_overflow():
     """Test that multiclass computations does not overflow even on byte input."""
     preds = torch.randint(20, (100,)).byte()