Bump torchmetrics from 0.11.4 to 1.0.0 in /requirements

pyproject.toml

@@ -68,7 +68,7 @@ dependencies = [
     # torch 1.12+ required by torchvision
     "torch>=1.12,<3",
     # torchmetrics 0.10+ required for binary/multiclass/multilabel classification metrics
-    "torchmetrics>=0.10,<0.12",
+    "torchmetrics>=0.10,<2",
     # torchvision 0.13+ required for torchvision.models._api.WeightsEnum
     "torchvision>=0.13,<0.16",
 ]

requirements/required.txt

@@ -17,5 +17,5 @@ segmentation-models-pytorch==0.3.3
 shapely==2.0.1
 timm==0.9.2
 torch==2.0.1
-torchmetrics==0.11.4
+torchmetrics==1.0.0
 torchvision==0.15.2

torchgeo/trainers/detection.py

@@ -12,6 +12,7 @@
 from lightning.pytorch import LightningModule
 from torch import Tensor
 from torch.optim.lr_scheduler import ReduceLROnPlateau
+from torchmetrics import MetricCollection
 from torchmetrics.detection.mean_ap import MeanAveragePrecision
 from torchvision.models import resnet as R
 from torchvision.models.detection.backbone_utils import resnet_fpn_backbone
@@ -187,8 +188,9 @@ def __init__(self, **kwargs: Any) -> None:
 
         self.config_task()
 
-        self.val_metrics = MeanAveragePrecision()
-        self.test_metrics = MeanAveragePrecision()
+        metrics = MetricCollection([MeanAveragePrecision()])
+        self.val_metrics = metrics.clone(prefix="val_")
+        self.test_metrics = metrics.clone(prefix="test_")
 
     def forward(self, *args: Any, **kwargs: Any) -> Any:
         """Forward pass of the model.
@@ -273,8 +275,11 @@ def validation_step(self, *args: Any, **kwargs: Any) -> None:
     def on_validation_epoch_end(self) -> None:
         """Logs epoch level validation metrics."""
         metrics = self.val_metrics.compute()
-        renamed_metrics = {f"val_{i}": metrics[i] for i in metrics.keys()}
-        self.log_dict(renamed_metrics)
+
+        # https://github.com/Lightning-AI/torchmetrics/pull/1832#issuecomment-1623890714
+        metrics.pop("val_classes", None)
+
+        self.log_dict(metrics)
         self.val_metrics.reset()
 
     def test_step(self, *args: Any, **kwargs: Any) -> None:
@@ -297,8 +302,11 @@ def test_step(self, *args: Any, **kwargs: Any) -> None:
     def on_test_epoch_end(self) -> None:
         """Logs epoch level test metrics."""
         metrics = self.test_metrics.compute()
-        renamed_metrics = {f"test_{i}": metrics[i] for i in metrics.keys()}
-        self.log_dict(renamed_metrics)
+
+        # https://github.com/Lightning-AI/torchmetrics/pull/1832#issuecomment-1623890714
+        metrics.pop("test_classes", None)
+
+        self.log_dict(metrics)
         self.test_metrics.reset()
 
     def predict_step(self, *args: Any, **kwargs: Any) -> list[dict[str, Tensor]]:

torchgeo/trainers/segmentation.py

@@ -175,7 +175,7 @@ class and used with 'ce' loss
                 MulticlassAccuracy(
                     num_classes=self.hyperparams["num_classes"],
                     ignore_index=self.ignore_index,
-                    mdmc_average="global",
+                    multidim_average="global",
                     average="micro",
                 ),
                 MulticlassJaccardIndex(