mypy: even more strict

pyproject.toml

@@ -141,27 +141,42 @@ exclude_lines = [
     "@overload",
 ]
 
+# https://mypy.readthedocs.io/en/stable/config_file.html
 [tool.mypy]
-python_version = "3.10"
+# Import discovery
 ignore_missing_imports = true
-show_error_codes = true
-exclude = "(build|data|dist|docs/src|images|logo|logs|output)/"
+exclude = "(build|data|dist|docs/.*|images|logo|.*logs|output|requirements)/"
 
-# Strict
-warn_unused_configs = true
+# Disallow dynamic typing (TODO: work in progress)
+disallow_any_unimported = false
+disallow_any_expr = false
+disallow_any_decorated = false
+disallow_any_explicit = false
 disallow_any_generics = true
 disallow_subclassing_any = true
+
+# Untyped definitions and calls
 disallow_untyped_calls = true
 disallow_untyped_defs = true
 disallow_incomplete_defs = true
-check_untyped_defs = true
 disallow_untyped_decorators = true
-no_implicit_optional = true
+
+# Configuring warnings
 warn_redundant_casts = true
 warn_unused_ignores = true
+warn_no_return = true
 warn_return_any = true
-no_implicit_reexport = true
+warn_unreachable = true
+
+# Miscellaneous strictness flags
 strict_equality = true
+strict = true
+
+# Configuring error messages
+pretty = true
+
+# Miscellaneous
+warn_unused_configs = true
 
 [tool.pytest.ini_options]
 # Skip slow tests by default

torchgeo/datasets/utils.py

@@ -14,7 +14,7 @@
 import shutil
 import subprocess
 import sys
-from collections.abc import Iterable, Iterator, Sequence
+from collections.abc import Iterable, Iterator, Mapping, MutableMapping, Sequence
 from dataclasses import dataclass
 from datetime import datetime, timedelta
 from typing import Any, TypeAlias, cast, overload
@@ -367,7 +367,9 @@ def working_dir(dirname: Path, create: bool = False) -> Iterator[None]:
         os.chdir(cwd)
 
 
-def _list_dict_to_dict_list(samples: Iterable[dict[Any, Any]]) -> dict[Any, list[Any]]:
+def _list_dict_to_dict_list(
+    samples: Iterable[Mapping[Any, Any]],
+) -> dict[Any, list[Any]]:
     """Convert a list of dictionaries to a dictionary of lists.
 
     Args:
@@ -385,7 +387,9 @@ def _list_dict_to_dict_list(samples: Iterable[dict[Any, Any]]) -> dict[Any, list
     return collated
 
 
-def _dict_list_to_list_dict(sample: dict[Any, Sequence[Any]]) -> list[dict[Any, Any]]:
+def _dict_list_to_list_dict(
+    sample: Mapping[Any, Sequence[Any]],
+) -> list[dict[Any, Any]]:
     """Convert a dictionary of lists to a list of dictionaries.
 
     Args:
@@ -405,7 +409,7 @@ def _dict_list_to_list_dict(sample: dict[Any, Sequence[Any]]) -> list[dict[Any,
     return uncollated
 
 
-def stack_samples(samples: Iterable[dict[Any, Any]]) -> dict[Any, Any]:
+def stack_samples(samples: Iterable[Mapping[Any, Any]]) -> dict[Any, Any]:
     """Stack a list of samples along a new axis.
 
     Useful for forming a mini-batch of samples to pass to
@@ -426,7 +430,7 @@ def stack_samples(samples: Iterable[dict[Any, Any]]) -> dict[Any, Any]:
     return collated
 
 
-def concat_samples(samples: Iterable[dict[Any, Any]]) -> dict[Any, Any]:
+def concat_samples(samples: Iterable[Mapping[Any, Any]]) -> dict[Any, Any]:
     """Concatenate a list of samples along an existing axis.
 
     Useful for joining samples in a :class:`torchgeo.datasets.IntersectionDataset`.
@@ -448,7 +452,7 @@ def concat_samples(samples: Iterable[dict[Any, Any]]) -> dict[Any, Any]:
     return collated
 
 
-def merge_samples(samples: Iterable[dict[Any, Any]]) -> dict[Any, Any]:
+def merge_samples(samples: Iterable[Mapping[Any, Any]]) -> dict[Any, Any]:
     """Merge a list of samples.
 
     Useful for joining samples in a :class:`torchgeo.datasets.UnionDataset`.
@@ -473,7 +477,7 @@ def merge_samples(samples: Iterable[dict[Any, Any]]) -> dict[Any, Any]:
     return collated
 
 
-def unbind_samples(sample: dict[Any, Sequence[Any]]) -> list[dict[Any, Any]]:
+def unbind_samples(sample: MutableMapping[Any, Any]) -> list[dict[Any, Any]]:
     """Reverse of :func:`stack_samples`.
 
     Useful for turning a mini-batch of samples into a list of samples. These individual

torchgeo/transforms/transforms.py

@@ -55,7 +55,7 @@ def __init__(
 
         self.augs = K.AugmentationSequential(*args, data_keys=keys, **kwargs)  # type: ignore[arg-type]
 
-    def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
+    def forward(self, batch: dict[str, Any]) -> dict[str, Any]:
         """Perform augmentations and update data dict.
 
         Args:
@@ -99,7 +99,7 @@ def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
 
         # Convert boxes to default [N, 4]
         if 'boxes' in batch:
-            batch['boxes'] = Boxes(batch['boxes']).to_tensor(mode='xyxy')  # type:ignore[assignment]
+            batch['boxes'] = Boxes(batch['boxes']).to_tensor(mode='xyxy')
 
         # Torchmetrics does not support masks with a channel dimension
         if 'mask' in batch and batch['mask'].shape[1] == 1: