Fix segmentation Dice + GeneralizedDice for 2d index tensors (#2832)

CHANGELOG.md

@@ -50,8 +50,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Removed `num_outputs` in `R2Score` ([#2800](https://github.com/Lightning-AI/torchmetrics/pull/2800))
 
+
 ### Fixed
 
+- Fixed segmentation `Dice` + `GeneralizedDice` for 2d index tensors ([#2832](https://github.com/Lightning-AI/torchmetrics/pull/2832))
+
+
 - Fixed mixed results of `rouge_score` with `accumulate='best'` ([#2830](https://github.com/Lightning-AI/torchmetrics/pull/2830))
 
 

src/torchmetrics/functional/segmentation/dice.py

@@ -49,13 +49,14 @@ def _dice_score_update(
 ) -> tuple[Tensor, Tensor, Tensor]:
     """Update the state with the current prediction and target."""
     _check_same_shape(preds, target)
-    if preds.ndim < 3:
-        raise ValueError(f"Expected both `preds` and `target` to have at least 3 dimensions, but got {preds.ndim}.")
 
     if input_format == "index":
         preds = torch.nn.functional.one_hot(preds, num_classes=num_classes).movedim(-1, 1)
         target = torch.nn.functional.one_hot(target, num_classes=num_classes).movedim(-1, 1)
 
+    if preds.ndim < 3:
+        raise ValueError(f"Expected both `preds` and `target` to have at least 3 dimensions, but got {preds.ndim}.")
+
     if not include_background:
         preds, target = _ignore_background(preds, target)
 

src/torchmetrics/functional/segmentation/generalized_dice.py

@@ -11,6 +11,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Tuple
+
 import torch
 from torch import Tensor
 from typing_extensions import Literal
@@ -49,16 +51,17 @@ def _generalized_dice_update(
     include_background: bool,
     weight_type: Literal["square", "simple", "linear"] = "square",
     input_format: Literal["one-hot", "index"] = "one-hot",
-) -> Tensor:
+) -> Tuple[Tensor, Tensor]:
     """Update the state with the current prediction and target."""
     _check_same_shape(preds, target)
-    if preds.ndim < 3:
-        raise ValueError(f"Expected both `preds` and `target` to have at least 3 dimensions, but got {preds.ndim}.")
 
     if input_format == "index":
         preds = torch.nn.functional.one_hot(preds, num_classes=num_classes).movedim(-1, 1)
         target = torch.nn.functional.one_hot(target, num_classes=num_classes).movedim(-1, 1)
 
+    if preds.ndim < 3:
+        raise ValueError(f"Expected both `preds` and `target` to have at least 3 dimensions, but got {preds.ndim}.")
+
     if not include_background:
         preds, target = _ignore_background(preds, target)
 
@@ -67,7 +70,6 @@ def _generalized_dice_update(
     target_sum = torch.sum(target, dim=reduce_axis)
     pred_sum = torch.sum(preds, dim=reduce_axis)
     cardinality = target_sum + pred_sum
-
     if weight_type == "simple":
         weights = 1.0 / target_sum
     elif weight_type == "linear":
@@ -89,7 +91,7 @@ def _generalized_dice_update(
 
     numerator = 2.0 * intersection * weights
     denominator = cardinality * weights
-    return numerator, denominator  # type:ignore[return-value]
+    return numerator, denominator
 
 
 def _generalized_dice_compute(numerator: Tensor, denominator: Tensor, per_class: bool = True) -> Tensor:

tests/unittests/segmentation/inputs.py

@@ -34,3 +34,7 @@
     preds=torch.randint(0, NUM_CLASSES, (NUM_BATCHES, BATCH_SIZE, 32, 32)),
     target=torch.randint(0, NUM_CLASSES, (NUM_BATCHES, BATCH_SIZE, 32, 32)),
 )
+_input4 = _Input(
+    preds=torch.randint(0, NUM_CLASSES, (NUM_BATCHES, BATCH_SIZE, 32)),
+    target=torch.randint(0, NUM_CLASSES, (NUM_BATCHES, BATCH_SIZE, 32)),
+)

tests/unittests/segmentation/test_dice.py

@@ -22,7 +22,7 @@
 from unittests import NUM_CLASSES
 from unittests._helpers import seed_all
 from unittests._helpers.testers import MetricTester
-from unittests.segmentation.inputs import _inputs1, _inputs2, _inputs3
+from unittests.segmentation.inputs import _input4, _inputs1, _inputs2, _inputs3
 
 seed_all(42)
 
@@ -55,6 +55,7 @@ def _reference_dice_score(
         (_inputs1.preds, _inputs1.target, "one-hot"),
         (_inputs2.preds, _inputs2.target, "one-hot"),
         (_inputs3.preds, _inputs3.target, "index"),
+        (_input4.preds, _input4.target, "index"),
     ],
 )
 @pytest.mark.parametrize("include_background", [True, False])

tests/unittests/segmentation/test_generalized_dice_score.py

@@ -24,7 +24,7 @@
 from unittests import NUM_CLASSES
 from unittests._helpers import seed_all
 from unittests._helpers.testers import MetricTester
-from unittests.segmentation.inputs import _inputs1, _inputs2, _inputs3
+from unittests.segmentation.inputs import _input4, _inputs1, _inputs2, _inputs3
 
 seed_all(42)
 
@@ -53,6 +53,7 @@ def _reference_generalized_dice(
         (_inputs1.preds, _inputs1.target, "one-hot"),
         (_inputs2.preds, _inputs2.target, "one-hot"),
         (_inputs3.preds, _inputs3.target, "index"),
+        (_input4.preds, _input4.target, "index"),
     ],
 )
 @pytest.mark.parametrize("include_background", [True, False])