Merge branch 'master' into master

CHANGELOG.md

@@ -55,6 +55,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Renamed `torchmetrics.collections` to `torchmetrics.metrics_collections` to avoid clashing with system's `collections` package ([#695](https://github.com/PyTorchLightning/metrics/pull/695))
 
 
+- Changed dtype of metric state from `torch.float` to `torch.long` in `ConfusionMatrix` to accommodate larger values ([#708](https://github.com/PyTorchLightning/metrics/issues/708))
+
+
 ### Deprecated
 
 - Renamed IoU -> Jaccard Index ([#662](https://github.com/PyTorchLightning/metrics/pull/662))

torchmetrics/classification/confusion_matrix.py

@@ -69,26 +69,26 @@ class ConfusionMatrix(Metric):
         >>> preds = torch.tensor([0, 1, 0, 0])
         >>> confmat = ConfusionMatrix(num_classes=2)
         >>> confmat(preds, target)
-        tensor([[2., 0.],
-                [1., 1.]])
+        tensor([[2, 0],
+                [1, 1]])
 
     Example (multiclass data):
         >>> target = torch.tensor([2, 1, 0, 0])
         >>> preds = torch.tensor([2, 1, 0, 1])
         >>> confmat = ConfusionMatrix(num_classes=3)
         >>> confmat(preds, target)
-        tensor([[1., 1., 0.],
-                [0., 1., 0.],
-                [0., 0., 1.]])
+        tensor([[1, 1, 0],
+                [0, 1, 0],
+                [0, 0, 1]])
 
     Example (multilabel data):
         >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
         >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
         >>> confmat = ConfusionMatrix(num_classes=3, multilabel=True)
         >>> confmat(preds, target)  # doctest: +NORMALIZE_WHITESPACE
-        tensor([[[1., 0.], [0., 1.]],
-                [[1., 0.], [1., 0.]],
-                [[0., 1.], [0., 1.]]])
+        tensor([[[1, 0], [0, 1]],
+                [[1, 0], [1, 0]],
+                [[0, 1], [0, 1]]])
 
     """
     is_differentiable = False
@@ -118,7 +118,10 @@ def __init__(
         if self.normalize not in allowed_normalize:
             raise ValueError(f"Argument average needs to one of the following: {allowed_normalize}")
 
-        default = torch.zeros(num_classes, 2, 2) if multilabel else torch.zeros(num_classes, num_classes)
+        if multilabel:
+            default = torch.zeros(num_classes, 2, 2, dtype=torch.long)
+        else:
+            default = torch.zeros(num_classes, num_classes, dtype=torch.long)
         self.add_state("confmat", default=default, dist_reduce_fx="sum")
 
     def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore

torchmetrics/functional/classification/confusion_matrix.py

@@ -158,26 +158,26 @@ def confusion_matrix(
         >>> preds = torch.tensor([0, 1, 0, 0])
         >>> confmat = ConfusionMatrix(num_classes=2)
         >>> confmat(preds, target)
-        tensor([[2., 0.],
-                [1., 1.]])
+        tensor([[2, 0],
+                [1, 1]])
 
     Example (multiclass data):
         >>> target = torch.tensor([2, 1, 0, 0])
         >>> preds = torch.tensor([2, 1, 0, 1])
         >>> confmat = ConfusionMatrix(num_classes=3)
         >>> confmat(preds, target)
-        tensor([[1., 1., 0.],
-                [0., 1., 0.],
-                [0., 0., 1.]])
+        tensor([[1, 1, 0],
+                [0, 1, 0],
+                [0, 0, 1]])
 
     Example (multilabel data):
         >>> target = torch.tensor([[0, 1, 0], [1, 0, 1]])
         >>> preds = torch.tensor([[0, 0, 1], [1, 0, 1]])
         >>> confmat = ConfusionMatrix(num_classes=3, multilabel=True)
         >>> confmat(preds, target)  # doctest: +NORMALIZE_WHITESPACE
-        tensor([[[1., 0.], [0., 1.]],
-                [[1., 0.], [1., 0.]],
-                [[0., 1.], [0., 1.]]])
+        tensor([[[1, 0], [0, 1]],
+                [[1, 0], [1, 0]],
+                [[0, 1], [0, 1]]])
 
     """
     confmat = _confusion_matrix_update(preds, target, num_classes, threshold, multilabel)