Update python (#1984)

* Empty-Commit

* Updating python

* More fixes

* Fix

* Cleanup

* Raised pytorch version

* Removed test for python 3.13

.github/workflows/ci.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         operating-system: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: [3.8, 3.9, "3.10", "3.11", "3.12"]
+        python-version: [3.9, "3.10", "3.11", "3.12"]
         include:
           - operating-system: ubuntu-latest
             path: ~/.cache/pip
@@ -54,11 +54,11 @@ jobs:
         matrix.operating-system == 'ubuntu-latest' ||
         matrix.operating-system == 'windows-latest'
       run: >
-        uv pip install --system --verbose torch==2.3.1+cpu torchvision==0.18.1+cpu --extra-index-url https://download.pytorch.org/whl/cpu
+        uv pip install --system --verbose torch==2.4.1+cpu torchvision==0.19.1+cpu --extra-index-url https://download.pytorch.org/whl/cpu
     - name: Install PyTorch on MacOS
       if: matrix.operating-system == 'macos-latest'
       run: >
-        uv pip install --system --verbose torch==2.3.1 torchvision==0.18.1
+        uv pip install --system --verbose torch==2.4.1 torchvision==0.19.1
     - name: Install dev requirements
       run: uv pip install --system -r requirements-dev.txt
     - name: Install dependencies
@@ -79,7 +79,7 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        python-version: ["3.8"]
+        python-version: ["3.9"]
     steps:
     - name: Checkout
       uses: actions/checkout@v4
@@ -105,7 +105,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8]
+        python-version: [3.9]
     steps:
     - name: Checkout
       uses: actions/checkout@v4

.github/workflows/upload_to_pypi.yml

@@ -12,7 +12,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v5
       with:
-        python-version: '3.8'
+        python-version: '3.9'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.readthedocs.yml

@@ -9,7 +9,7 @@ formats:
   - epub
 
 python:
-  version: 3.8
+  version: 3.9
   system_packages: true
   install:
     - requirements: docs/requirements.txt

albumentations/augmentations/blur/functional.py

@@ -1,8 +1,9 @@
 from __future__ import annotations
 
+from collections.abc import Sequence
 from itertools import product
 from math import ceil
-from typing import Literal, Sequence
+from typing import Literal
 
 import cv2
 import numpy as np

albumentations/augmentations/blur/transforms.py

@@ -2,7 +2,7 @@
 
 import random
 import warnings
-from typing import Any, Literal, Tuple, cast
+from typing import Any, Literal, cast
 
 import cv2
 import numpy as np
@@ -97,7 +97,7 @@ class InitSchema(BlurInitSchema):
 
     def __init__(self, blur_limit: ScaleIntType = (3, 7), p: float = 0.5, always_apply: bool | None = None):
         super().__init__(p=p, always_apply=always_apply)
-        self.blur_limit = cast(Tuple[int, int], blur_limit)
+        self.blur_limit = cast(tuple[int, int], blur_limit)
 
     def apply(self, img: np.ndarray, kernel: int, **params: Any) -> np.ndarray:
         return fblur.blur(img, kernel)
@@ -187,7 +187,7 @@ def __init__(
     ):
         super().__init__(blur_limit=blur_limit, p=p, always_apply=always_apply)
         self.allow_shifted = allow_shifted
-        self.blur_limit = cast(Tuple[int, int], blur_limit)
+        self.blur_limit = cast(tuple[int, int], blur_limit)
 
     def get_transform_init_args_names(self) -> tuple[str, ...]:
         return (*super().get_transform_init_args_names(), "allow_shifted")
@@ -387,8 +387,8 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(p, always_apply)
-        self.blur_limit = cast(Tuple[int, int], blur_limit)
-        self.sigma_limit = cast(Tuple[float, float], sigma_limit)
+        self.blur_limit = cast(tuple[int, int], blur_limit)
+        self.sigma_limit = cast(tuple[float, float], sigma_limit)
 
     def apply(self, img: np.ndarray, ksize: int, sigma: float, **params: Any) -> np.ndarray:
         return fblur.gaussian_blur(img, ksize, sigma=sigma)
@@ -639,12 +639,12 @@ def __init__(
             warnings.warn("sigmaY_limit is deprecated; use sigma_y_limit instead.", DeprecationWarning, stacklevel=2)
             sigma_y_limit = sigmaY_limit
 
-        self.blur_limit = cast(Tuple[int, int], blur_limit)
-        self.sigma_x_limit = cast(Tuple[float, float], sigma_x_limit)
-        self.sigma_y_limit = cast(Tuple[float, float], sigma_y_limit)
-        self.rotate_limit = cast(Tuple[int, int], rotate_limit)
-        self.beta_limit = cast(Tuple[float, float], beta_limit)
-        self.noise_limit = cast(Tuple[float, float], noise_limit)
+        self.blur_limit = cast(tuple[int, int], blur_limit)
+        self.sigma_x_limit = cast(tuple[float, float], sigma_x_limit)
+        self.sigma_y_limit = cast(tuple[float, float], sigma_y_limit)
+        self.rotate_limit = cast(tuple[int, int], rotate_limit)
+        self.beta_limit = cast(tuple[float, float], beta_limit)
+        self.noise_limit = cast(tuple[float, float], noise_limit)
 
     def apply(self, img: np.ndarray, kernel: np.ndarray, **params: Any) -> np.ndarray:
         return fmain.convolve(img, kernel=kernel)
@@ -754,8 +754,8 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(p, always_apply)
-        self.radius = cast(Tuple[int, int], radius)
-        self.alias_blur = cast(Tuple[float, float], alias_blur)
+        self.radius = cast(tuple[int, int], radius)
+        self.alias_blur = cast(tuple[float, float], alias_blur)
 
     def apply(self, img: np.ndarray, radius: int, alias_blur: float, **params: Any) -> np.ndarray:
         return fblur.defocus(img, radius, alias_blur)
@@ -804,8 +804,8 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(p, always_apply)
-        self.max_factor = cast(Tuple[float, float], max_factor)
-        self.step_factor = cast(Tuple[float, float], step_factor)
+        self.max_factor = cast(tuple[float, float], max_factor)
+        self.step_factor = cast(tuple[float, float], step_factor)
 
     def apply(self, img: np.ndarray, zoom_factors: np.ndarray, **params: Any) -> np.ndarray:
         return fblur.zoom_blur(img, zoom_factors)

albumentations/augmentations/crops/functional.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import cv2
 import numpy as np

albumentations/augmentations/crops/transforms.py

@@ -2,13 +2,14 @@
 
 import math
 import random
-from typing import Any, Sequence, Tuple, cast
+from collections.abc import Sequence
+from typing import Annotated, Any, cast
 from warnings import warn
 
 import cv2
 import numpy as np
 from pydantic import AfterValidator, Field, field_validator, model_validator
-from typing_extensions import Annotated, Self
+from typing_extensions import Self
 
 from albumentations.augmentations.geometric import functional as fgeometric
 from albumentations.core.bbox_utils import union_of_bboxes
@@ -583,7 +584,7 @@ def __init__(
         always_apply: bool | None = None,
         p: float = 1.0,
     ):
-        super().__init__(size=cast(Tuple[int, int], size), interpolation=interpolation, p=p, always_apply=always_apply)
+        super().__init__(size=cast(tuple[int, int], size), interpolation=interpolation, p=p, always_apply=always_apply)
         self.min_max_height = min_max_height
         self.w2h_ratio = w2h_ratio
 
@@ -714,7 +715,7 @@ def __init__(
         always_apply: bool | None = None,
         p: float = 1.0,
     ):
-        super().__init__(size=cast(Tuple[int, int], size), interpolation=interpolation, p=p, always_apply=always_apply)
+        super().__init__(size=cast(tuple[int, int], size), interpolation=interpolation, p=p, always_apply=always_apply)
         self.scale = scale
         self.ratio = ratio
 
@@ -828,7 +829,7 @@ def __init__(
             # Ensure the new parameter is used even if the old one is passed
             cropping_bbox_key = cropping_box_key
 
-        self.max_part_shift = cast(Tuple[float, float], max_part_shift)
+        self.max_part_shift = cast(tuple[float, float], max_part_shift)
         self.cropping_bbox_key = cropping_bbox_key
 
     def get_params_dependent_on_data(

albumentations/augmentations/domain_adaptation/transforms.py

@@ -1,13 +1,13 @@
 from __future__ import annotations
 
 import random
-from typing import Any, Callable, Literal, Sequence, Tuple, cast
+from collections.abc import Sequence
+from typing import Annotated, Any, Callable, Literal, cast
 
 import cv2
 import numpy as np
 from albucore import add_weighted, get_num_channels
 from pydantic import AfterValidator, Field, field_validator
-from typing_extensions import Annotated
 
 import albumentations.augmentations.geometric.functional as fgeometric
 from albumentations.augmentations.domain_adaptation.functional import (
@@ -205,7 +205,7 @@ def __init__(
         super().__init__(p=p, always_apply=always_apply)
         self.reference_images = reference_images
         self.read_fn = read_fn
-        self.beta_limit = cast(Tuple[float, float], beta_limit)
+        self.beta_limit = cast(tuple[float, float], beta_limit)
 
     def apply(
         self,
@@ -468,7 +468,7 @@ def __init__(
     ):
         super().__init__(p=p, always_apply=always_apply)
         self.templates = templates
-        self.img_weight = cast(Tuple[float, float], img_weight)
+        self.img_weight = cast(tuple[float, float], img_weight)
         self.template_transform = template_transform
         self.name = name
 

albumentations/augmentations/dropout/channel_dropout.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
 import random
-from typing import Any, Mapping
+from collections.abc import Mapping
+from typing import Annotated, Any
 
 import numpy as np
 from albucore import get_num_channels
 from pydantic import AfterValidator, Field
-from typing_extensions import Annotated
 
 from albumentations.core.pydantic import check_1plus
 from albumentations.core.transforms_interface import BaseTransformInitSchema, ImageOnlyTransform

albumentations/augmentations/dropout/coarse_dropout.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
 import random
-from typing import Any
+from typing import Annotated, Any
 from warnings import warn
 
 import numpy as np
 from pydantic import AfterValidator, Field, model_validator
-from typing_extensions import Annotated, Literal, Self
+from typing_extensions import Literal, Self
 
 from albumentations import random_utils
 from albumentations.augmentations.dropout.transforms import BaseDropout

albumentations/augmentations/dropout/grid_dropout.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from typing import Any, Literal
+from typing import Annotated, Any, Literal
 from warnings import warn
 
 from pydantic import AfterValidator, Field, model_validator
-from typing_extensions import Annotated, Self
+from typing_extensions import Self
 
 import albumentations.augmentations.dropout.functional as fdropout
 from albumentations import random_utils

albumentations/augmentations/dropout/mask_dropout.py

@@ -1,12 +1,11 @@
 from __future__ import annotations
 
 import random
-from typing import Any, Tuple, cast
+from typing import Any, Literal, cast
 
 import cv2
 import numpy as np
 from skimage.measure import label
-from typing_extensions import Literal
 
 import albumentations.augmentations.dropout.functional as fdropout
 from albumentations.core.bbox_utils import BboxProcessor, denormalize_bboxes, normalize_bboxes
@@ -88,7 +87,7 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(p=p, always_apply=always_apply)
-        self.max_objects = cast(Tuple[int, int], max_objects)
+        self.max_objects = cast(tuple[int, int], max_objects)
         self.image_fill_value = image_fill_value
         self.mask_fill_value = mask_fill_value
 

albumentations/augmentations/dropout/xy_masking.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import random
-from typing import Any, Tuple, cast
+from typing import Any, cast
 
 import numpy as np
 from pydantic import model_validator
@@ -86,11 +86,11 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(p=p, always_apply=always_apply, fill_value=fill_value, mask_fill_value=mask_fill_value)
-        self.num_masks_x = cast(Tuple[int, int], num_masks_x)
-        self.num_masks_y = cast(Tuple[int, int], num_masks_y)
+        self.num_masks_x = cast(tuple[int, int], num_masks_x)
+        self.num_masks_y = cast(tuple[int, int], num_masks_y)
 
-        self.mask_x_length = cast(Tuple[int, int], mask_x_length)
-        self.mask_y_length = cast(Tuple[int, int], mask_y_length)
+        self.mask_x_length = cast(tuple[int, int], mask_x_length)
+        self.mask_y_length = cast(tuple[int, int], mask_y_length)
 
     def validate_mask_length(
         self,

albumentations/augmentations/functional.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 from collections import defaultdict
-from typing import Any, Literal, Sequence
+from collections.abc import Sequence
+from typing import Any, Literal
 from warnings import warn
 
 import cv2

albumentations/augmentations/geometric/functional.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 import math
-from typing import Any, Callable, Literal, Sequence, Tuple, TypedDict, cast
+from collections.abc import Sequence
+from typing import Any, Callable, Literal, TypedDict, cast
 
 import cv2
 import numpy as np
@@ -2399,7 +2400,7 @@ def compute_affine_warp_output_shape(
     height, width = input_shape[:2]
 
     if height == 0 or width == 0:
-        return matrix, cast(Tuple[int, int], input_shape[:2])
+        return matrix, cast(tuple[int, int], input_shape[:2])
 
     min_coords, max_coords = compute_transformed_image_bounds(matrix, (height, width))
     minc, minr = min_coords
@@ -2418,7 +2419,7 @@ def compute_affine_warp_output_shape(
     translation = -minc, -minr
     matrix_to_fit = skimage.transform.SimilarityTransform(translation=translation)
     matrix += matrix_to_fit
-    return matrix, cast(Tuple[int, int], output_shape_tuple)
+    return matrix, cast(tuple[int, int], output_shape_tuple)
 
 
 def center(image_shape: tuple[int, int]) -> tuple[float, float]:

albumentations/augmentations/geometric/resize.py

@@ -1,7 +1,8 @@
 from __future__ import annotations
 
 import random
-from typing import Any, List, Sequence, Tuple, Union, cast
+from collections.abc import Sequence
+from typing import Any, Union, cast
 
 import cv2
 import numpy as np
@@ -78,7 +79,7 @@ def __init__(
         p: float = 0.5,
     ):
         super().__init__(p=p, always_apply=always_apply)
-        self.scale_limit = cast(Tuple[float, float], scale_limit)
+        self.scale_limit = cast(tuple[float, float], scale_limit)
         self.interpolation = interpolation
 
     def get_params(self) -> dict[str, float]:
@@ -129,7 +130,7 @@ def check_scale_limit(cls, v: ScaleIntType, info: ValidationInfo) -> int | list[
             if not value >= 1:
                 raise ValueError(f"{info.field_name} must be bigger or equal to 1.")
 
-        return cast(Union[int, List[int]], result)
+        return cast(Union[int, list[int]], result)
 
 
 class LongestMaxSize(DualTransform):