build(deps-dev): bump mypy from 1.8.0 to 1.10.0 (#243)

* build(deps): bump mypy from 1.8.0 to 1.10.0

Bumps [mypy](https://github.com/python/mypy) from 1.8.0 to 1.10.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](python/mypy@v1.8.0...v1.10.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <[email protected]>

* style(mypy): add types-Pillow

* style(mypy): update type annotations

---------

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: F-G Fernandez <[email protected]>

pyproject.toml

@@ -50,7 +50,8 @@ test = [
 ]
 quality = [
     "ruff==0.4.1",
-    "mypy==1.8.0",
+    "mypy==1.10.0",
+    "types-Pillow",
     "pre-commit>=3.0.0,<4.0.0",
 ]
 docs = [
@@ -77,7 +78,8 @@ dev = [
     "pytest-pretty>=1.0.0,<2.0.0",
     # style
     "ruff==0.4.1",
-    "mypy==1.8.0",
+    "mypy==1.10.0",
+    "types-Pillow",
     "pre-commit>=3.0.0,<4.0.0",
     # docs
     "sphinx>=3.0.0,!=3.5.0",

torchcam/methods/core.py

@@ -7,7 +7,7 @@
 from abc import abstractmethod
 from functools import partial
 from types import TracebackType
-from typing import Any, List, Optional, Tuple, Type, Union, cast
+from typing import Any, List, Optional, Tuple, Type, Union
 
 import torch
 import torch.nn.functional as F
@@ -267,4 +267,4 @@ def _fuse_cams(cams: List[Tensor], target_shape: Tuple[int, int]) -> Tensor:
         ]
 
         # Fuse them
-        return cast(Tensor, torch.stack(scaled_cams).max(dim=0).values.squeeze(1))
+        return torch.stack(scaled_cams).max(dim=0).values.squeeze(1)

torchcam/methods/gradient.py

@@ -4,7 +4,7 @@
 # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
 
 from functools import partial
-from typing import Any, List, Optional, Tuple, Union, cast
+from typing import Any, List, Optional, Tuple, Union
 
 import torch
 from torch import Tensor, nn
@@ -404,4 +404,4 @@ def _get_weights(self, class_idx: Union[int, List[int]], scores: Tensor, **kwarg
     @staticmethod
     def _scale_cams(cams: List[Tensor], gamma: float = 2.0) -> List[Tensor]:
         # cf. Equation 9 in the paper
-        return [torch.tanh(cast(Tensor, gamma * cam)) for cam in cams]
+        return [torch.tanh(gamma * cam) for cam in cams]

torchcam/utils.py

@@ -3,12 +3,14 @@
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
 
+from typing import cast
+
 import numpy as np
 from matplotlib import colormaps as cm
-from PIL import Image
+from PIL.Image import Image, Resampling, fromarray
 
 
-def overlay_mask(img: Image.Image, mask: Image.Image, colormap: str = "jet", alpha: float = 0.7) -> Image.Image:
+def overlay_mask(img: Image, mask: Image, colormap: str = "jet", alpha: float = 0.7) -> Image:
     """Overlay a colormapped mask on a background image
 
     >>> from PIL import Image
@@ -31,17 +33,15 @@ def overlay_mask(img: Image.Image, mask: Image.Image, colormap: str = "jet", alp
         TypeError: when the arguments have invalid types
         ValueError: when the alpha argument has an incorrect value
     """
-    if not isinstance(img, Image.Image) or not isinstance(mask, Image.Image):
+    if not isinstance(img, Image) or not isinstance(mask, Image):
         raise TypeError("img and mask arguments need to be PIL.Image")
 
     if not isinstance(alpha, float) or alpha < 0 or alpha >= 1:
         raise ValueError("alpha argument is expected to be of type float between 0 and 1")
 
     cmap = cm.get_cmap(colormap)
     # Resize mask and apply colormap
-    overlay = mask.resize(img.size, resample=Image.BICUBIC)
+    overlay = mask.resize(img.size, resample=Resampling.BICUBIC)
     overlay = (255 * cmap(np.asarray(overlay) ** 2)[:, :, :3]).astype(np.uint8)
     # Overlay the image with the mask
-    overlayed_img = Image.fromarray((alpha * np.asarray(img) + (1 - alpha) * overlay).astype(np.uint8))
-
-    return overlayed_img
+    return fromarray((alpha * np.asarray(img) + (1 - alpha) * cast(np.ndarray, overlay)).astype(np.uint8))