mypy v0.920 update (#299)

.pre-commit-config.yaml

@@ -24,7 +24,7 @@ repos:
             additional_dependencies: ["toml"]
 
     -   repo: https://github.com/pre-commit/mirrors-mypy
-        rev: v0.910
+        rev: v0.920
         hooks:
         -   id: mypy
             args: [--strict, --ignore-missing-imports, --show-error-codes]

benchmark.py

@@ -167,7 +167,7 @@ def main(args: argparse.Namespace) -> None:
             dataloader = DataLoader(
                 dataset,
                 batch_size=args.batch_size,
-                sampler=sampler,  # type: ignore[arg-type]
+                sampler=sampler,
                 num_workers=args.num_workers,
                 collate_fn=stack_samples,
             )

setup.cfg

@@ -102,9 +102,8 @@ style =
     # pydocstyle 6.1+ required for pyproject.toml support
     pydocstyle[toml]>=6.1
 tests =
-    # mypy 0.900+ required for pyproject.toml support, 0.920 has a decorator bug:
-    # https://github.com/python/mypy/issues/11763
-    mypy>=0.900,!=0.920
+    # mypy 0.900+ required for pyproject.toml support
+    mypy>=0.900
     # nbmake 0.1+ required to fix path_source bug
     nbmake>=0.1
     # pytest 6+ required for pyproject.toml support

torchgeo/datasets/idtrees.py

@@ -334,7 +334,7 @@ class IDTReeS(VisionDataset):
         else:
             directory = os.path.join(root, self.task)
             if self.task == "task1":
-                geoms = None  # type: ignore[assignment]
+                geoms = None
                 labels = None
             else:
                 geoms = self._load_geometries(directory)
@@ -342,7 +342,7 @@ class IDTReeS(VisionDataset):
 
         images = glob.glob(os.path.join(directory, "RemoteSensing", "RGB", "*.tif"))
 
-        return images, geoms, labels
+        return images, geoms, labels  # type: ignore[return-value]
 
     def _load_labels(self, directory: str) -> Any:
         """Load the csv files containing the labels.

torchgeo/datasets/oscd.py

@@ -196,7 +196,9 @@ class OSCD(VisionDataset):
         for path in paths:
             with Image.open(path) as img:
                 images.append(np.array(img))
-        array = np.stack(images, axis=0).astype(np.int_)
+        array: Array = np.stack(images, axis=0).astype(  # type: ignore[type-arg]
+            np.int_
+        )
         tensor: Tensor = torch.from_numpy(array)  # type: ignore[attr-defined]
         return tensor