Bump mypy from 0.991 to 1.0.0 in /requirements

.pre-commit-config.yaml

@@ -30,7 +30,7 @@ repos:
             additional_dependencies: ["toml"]
 
     -   repo: https://github.com/pre-commit/mirrors-mypy
-        rev: v0.991
+        rev: v1.0.0
         hooks:
         -   id: mypy
             args: [--strict, --ignore-missing-imports, --show-error-codes]

evaluate.py

@@ -119,7 +119,7 @@ def run_eval_loop(
                 }
                 for i in range(len(batch["image"]))
             ]
-        with torch.inference_mode():
+        with torch.inference_mode():  # type: ignore[no-untyped-call]
             y_pred = model(x)
         metrics(y_pred, y)
     results = metrics.compute()
@@ -143,7 +143,6 @@ def main(args: argparse.Namespace) -> None:
     # Loads the saved model from checkpoint based on the `args.task` name that was
     # passed as input
     model = TASK.load_from_checkpoint(args.input_checkpoint)
-    model = cast(pl.LightningModule, model)
     model.freeze()
     model.eval()
 

requirements/tests.txt

@@ -1,5 +1,5 @@
 # tests
-mypy==0.991
+mypy==1.0.0
 nbmake==1.3.5
 pytest==7.2.1
 pytest-cov==4.0.0

setup.cfg

@@ -121,7 +121,7 @@ style =
     pyupgrade>=2.4,<4
 tests =
     # mypy 0.900+ required for pyproject.toml support
-    mypy>=0.900,<0.992
+    mypy>=0.900,<2
     # nbmake 0.1+ required to fix path_source bug
     nbmake>=0.1,<2
     # pytest 6.1.2+ required by nbmake

tests/models/test_changestar.py

@@ -17,7 +17,7 @@
 
 
 class TestChangeStar:
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     def test_changestar_farseg_classes(self) -> None:
         model = ChangeStarFarSeg(
             classes=4, backbone="resnet50", backbone_pretrained=False
@@ -27,7 +27,7 @@ def test_changestar_farseg_classes(self) -> None:
 
         assert y["bi_seg_logit"].shape[2] == 4
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     def test_changestar_farseg_output_size(self) -> None:
         model = ChangeStarFarSeg(
             classes=4, backbone="resnet50", backbone_pretrained=False
@@ -55,7 +55,7 @@ def test_invalid_changestar_farseg_backbone(self) -> None:
         with pytest.raises(ValueError, match=match):
             ChangeStarFarSeg(classes=4, backbone="anynet", backbone_pretrained=False)
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize(
         "inc,innerc,nc,sf", list(itertools.product(IN_CHANNELS, INNNR_CHANNELS, NC, SF))
     )
@@ -70,7 +70,7 @@ def test_changemixin_output_size(
         assert y[0].shape == y[1].shape
         assert y[0].shape == (3, 1, 32 * sf, 32 * sf)
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     def test_changestar(self) -> None:
         dense_feature_extractor = nn.modules.Sequential(
             nn.modules.Conv2d(3, 32, 3, 1, 1),
@@ -97,7 +97,7 @@ def test_changestar(self) -> None:
         assert y["bi_seg_logit"].shape == (3, 2, 2, 64, 64)
         assert y["change_prob"].shape == (3, 1, 64, 64)
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     def test_changestar_invalid_inference_mode(self) -> None:
         dense_feature_extractor = nn.modules.Sequential(
             nn.modules.Conv2d(3, 32, 3, 1, 1),
@@ -122,7 +122,7 @@ def test_changestar_invalid_inference_mode(self) -> None:
                 inference_mode="random",
             )
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize("inference_mode", ["t1t2", "t2t1", "mean"])
     def test_changestar_inference_output_size(self, inference_mode: str) -> None:
         dense_feature_extractor = nn.modules.Sequential(

tests/models/test_farseg.py

@@ -9,7 +9,7 @@
 
 
 class TestFarSeg:
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize(
         "backbone,pretrained",
         [

tests/models/test_fcsiam.py

@@ -14,7 +14,7 @@
 
 
 class TestFCSiamConc:
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize("b, c", list(itertools.product(BATCH_SIZE, CHANNELS)))
     def test_in_channels(self, b: int, c: int) -> None:
         classes = 2
@@ -24,7 +24,7 @@ def test_in_channels(self, b: int, c: int) -> None:
         y = model(x)
         assert y.shape == (b, classes, h, w)
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize("b, classes", list(itertools.product(BATCH_SIZE, CLASSES)))
     def test_classes(self, b: int, classes: int) -> None:
         t, c, h, w = 2, 3, 64, 64
@@ -35,7 +35,7 @@ def test_classes(self, b: int, classes: int) -> None:
 
 
 class TestFCSiamDiff:
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize("b, c", list(itertools.product(BATCH_SIZE, CHANNELS)))
     def test_in_channels(self, b: int, c: int) -> None:
         classes = 2
@@ -45,7 +45,7 @@ def test_in_channels(self, b: int, c: int) -> None:
         y = model(x)
         assert y.shape == (b, classes, h, w)
 
-    @torch.no_grad()  # type: ignore[misc]
+    @torch.no_grad()
     @pytest.mark.parametrize("b, classes", list(itertools.product(BATCH_SIZE, CLASSES)))
     def test_classes(self, b: int, classes: int) -> None:
         t, c, h, w = 2, 3, 64, 64

torchgeo/datasets/utils.py

@@ -89,7 +89,7 @@ def __enter__(self) -> Any:
             except ImportError:
                 # Only supports normal zip files
                 # https://github.com/python/mypy/issues/1153
-                import zipfile  # type: ignore[no-redef]
+                import zipfile
 
             return zipfile.ZipFile(*self.args, **self.kwargs)
 

torchgeo/trainers/byol.py

@@ -337,7 +337,7 @@ def __init__(self, **kwargs: Any) -> None:
         super().__init__()
 
         # Creates `self.hparams` from kwargs
-        self.save_hyperparameters()  # type: ignore[operator]
+        self.save_hyperparameters()
         self.hyperparams = cast(Dict[str, Any], self.hparams)
 
         self.config_task()

torchgeo/trainers/classification.py

@@ -97,7 +97,7 @@ def __init__(self, **kwargs: Any) -> None:
         super().__init__()
 
         # Creates `self.hparams` from kwargs
-        self.save_hyperparameters()  # type: ignore[operator]
+        self.save_hyperparameters()
         self.hyperparams = cast(Dict[str, Any], self.hparams)
 
         self.config_task()

torchgeo/trainers/detection.py

@@ -164,7 +164,7 @@ def __init__(self, **kwargs: Any) -> None:
         """
         super().__init__()
         # Creates `self.hparams` from kwargs
-        self.save_hyperparameters()  # type: ignore[operator]
+        self.save_hyperparameters()
         self.hyperparams = cast(Dict[str, Any], self.hparams)
 
         self.config_task()

torchgeo/trainers/regression.py

@@ -75,7 +75,7 @@ def __init__(self, **kwargs: Any) -> None:
         super().__init__()
 
         # Creates `self.hparams` from kwargs
-        self.save_hyperparameters()  # type: ignore[operator]
+        self.save_hyperparameters()
         self.hyperparams = cast(Dict[str, Any], self.hparams)
         self.config_task()
 

torchgeo/trainers/segmentation.py

@@ -104,7 +104,7 @@ def __init__(self, **kwargs: Any) -> None:
         super().__init__()
 
         # Creates `self.hparams` from kwargs
-        self.save_hyperparameters()  # type: ignore[operator]
+        self.save_hyperparameters()
         self.hyperparams = cast(Dict[str, Any], self.hparams)
 
         if not isinstance(kwargs["ignore_index"], (int, type(None))):

torchgeo/transforms/transforms.py

@@ -5,11 +5,10 @@
 
 from typing import Any, Dict, List, Optional, Tuple, Union
 
-import kornia
+import kornia.augmentation as K
 import torch
 from einops import rearrange
-from kornia.augmentation import GeometricAugmentationBase2D
-from kornia.augmentation.random_generator import CropGenerator
+from kornia.constants import DataKey
 from kornia.geometry import crop_by_indices
 from torch import Tensor
 from torch.nn.modules import Module
@@ -23,17 +22,21 @@ class AugmentationSequential(Module):
        Use :class:`kornia.augmentation.container.AugmentationSequential` instead.
     """
 
-    def __init__(self, *args: Module, data_keys: List[str]) -> None:
+    def __init__(
+        self,
+        *args: Union[K.base._AugmentationBase, K.ImageSequential],
+        data_keys: List[str],
+    ) -> None:
         """Initialize a new augmentation sequential instance.
 
         Args:
             *args: Sequence of kornia augmentations
-            data_keys: List of inputs to augment (e.g. ["image", "mask", "boxes"])
+            data_keys: List of inputs to augment (e.g., ["image", "mask", "boxes"])
         """
         super().__init__()
         self.data_keys = data_keys
 
-        keys = []
+        keys: List[Union[str, int, DataKey]] = []
         for key in data_keys:
             if key == "image":
                 keys.append("input")
@@ -42,7 +45,7 @@ def __init__(self, *args: Module, data_keys: List[str]) -> None:
             else:
                 keys.append(key)
 
-        self.augs = kornia.augmentation.AugmentationSequential(*args, data_keys=keys)
+        self.augs = K.AugmentationSequential(*args, data_keys=keys)
 
     def forward(self, batch: Dict[str, Tensor]) -> Dict[str, Tensor]:
         """Perform augmentations and update data dict.
@@ -84,7 +87,7 @@ def forward(self, batch: Dict[str, Tensor]) -> Dict[str, Tensor]:
         return batch
 
 
-class _RandomNCrop(GeometricAugmentationBase2D):
+class _RandomNCrop(K.GeometricAugmentationBase2D):
     """Take N random crops of a tensor."""
 
     def __init__(self, size: Tuple[int, int], num: int) -> None:
@@ -138,7 +141,7 @@ def apply_transform(
         return torch.cat(out)
 
 
-class _NCropGenerator(CropGenerator):
+class _NCropGenerator(K.random_generator.CropGenerator):
     """Generate N random crops."""
 
     def __init__(self, size: Union[Tuple[int, int], Tensor], num: int) -> None: