Drop support for Python 3.10

.github/workflows/tests.yaml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ['3.10', '3.11', '3.12', '3.13']
+        python-version: ['3.11', '3.12', '3.13']
     steps:
       - name: Clone repo
         uses: actions/[email protected]
@@ -59,7 +59,7 @@ jobs:
         id: setup-python
         uses: actions/[email protected]
         with:
-          python-version: '3.10'
+          python-version: '3.11'
       - name: Cache dependencies
         uses: actions/[email protected]
         id: cache

pyproject.toml

@@ -10,7 +10,7 @@ build-backend = "setuptools.build_meta"
 name = "torchgeo"
 description = "TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 license = {file = "LICENSE"}
 authors = [
     {name = "Adam J. Stewart", email = "[email protected]"},
@@ -29,7 +29,6 @@ classifiers = [
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
@@ -39,8 +38,8 @@ classifiers = [
 dependencies = [
     # einops 0.3+ required for einops.repeat
     "einops>=0.3",
-    # fiona 1.8.21+ required for Python 3.10 wheels
-    "fiona>=1.8.21",
+    # fiona 1.8.22+ required for Python 3.11 wheels
+    "fiona>=1.8.22",
     # kornia 0.7.4+ required for AugmentationSequential support for unknown keys
     "kornia>=0.7.4",
     # lightly 1.4.5+ required for LARS optimizer
@@ -51,53 +50,53 @@ dependencies = [
     # lightning 2.3 contains known bugs related to YAML parsing
     # https://github.com/Lightning-AI/pytorch-lightning/issues/19977
     "lightning[pytorch-extra]>=2,!=2.3.*,!=2.5.0",
-    # matplotlib 3.5+ required for Python 3.10 wheels
-    "matplotlib>=3.5",
-    # numpy 1.21.2+ required by Python 3.10 wheels
-    "numpy>=1.21.2",
-    # pandas 1.3.3+ required for Python 3.10 wheels
-    "pandas>=1.3.3",
-    # pillow 8.4+ required for Python 3.10 wheels
-    "pillow>=8.4",
-    # pyproj 3.3+ required for Python 3.10 wheels
-    "pyproj>=3.3",
-    # rasterio 1.3+ required for Python 3.10 wheels
+    # matplotlib 3.6+ required for Python 3.11 wheels
+    "matplotlib>=3.6",
+    # numpy 1.23.2+ required by Python 3.11 wheels
+    "numpy>=1.23.2",
+    # pandas 1.5+ required for Python 3.11 wheels
+    "pandas>=1.5",
+    # pillow 9.2+ required for Python 3.11 wheels
+    "pillow>=9.2",
+    # pyproj 3.4+ required for Python 3.11 wheels
+    "pyproj>=3.4",
+    # rasterio 1.3.3+ required for Python 3.11 wheels
     # rasterio 1.4.0-1.4.2 lack support for merging WarpedVRT objects
     # https://github.com/rasterio/rasterio/issues/3196
-    "rasterio>=1.3,!=1.4.0,!=1.4.1,!=1.4.2",
-    # rtree 1+ required for Python 3.10 wheels
-    "rtree>=1",
+    "rasterio>=1.3.3,!=1.4.0,!=1.4.1,!=1.4.2",
+    # rtree 1.0.1+ required for Python 3.11 wheels
+    "rtree>=1.0.1",
     # segmentation-models-pytorch 0.2+ required for smp.losses module
     "segmentation-models-pytorch>=0.2",
-    # shapely 1.8+ required for Python 3.10 wheels
-    "shapely>=1.8",
+    # shapely 1.8.5+ required for Python 3.11 wheels
+    "shapely>=1.8.5",
     # timm 0.4.12 required by segmentation-models-pytorch
     "timm>=0.4.12",
-    # torch 1.13+ required by torchvision
-    "torch>=1.13",
+    # torch 2+ required for Python 3.11 wheels
+    "torch>=2",
     # torchmetrics 0.10+ required for binary/multiclass/multilabel classification metrics
     "torchmetrics>=0.10",
-    # torchvision 0.14+ required for torchvision.models.swin_v2_b
-    "torchvision>=0.14",
+    # torchvision 0.15.1+ required for Python 3.11 wheels
+    "torchvision>=0.15.1",
 ]
 dynamic = ["version"]
 
 [project.optional-dependencies]
 datasets = [
-    # h5py 3.6+ required for Python 3.10 wheels
-    "h5py>=3.6",
+    # h5py 3.8+ required for Python 3.11 wheels
+    "h5py>=3.8",
     # laspy 2+ required for laspy.read
     "laspy>=2",
-    # opencv-python 4.5.4+ required for Python 3.10 wheels
-    "opencv-python>=4.5.4",
+    # opencv-python 4.5.5+ required for Python 3.11 wheels
+    "opencv-python>=4.5.5",
     # pandas 2+ required for parquet extra
     "pandas[parquet]>=2",
-    # pycocotools 2.0.7+ required for wheels
+    # pycocotools 2.0.7+ required for Python 3.11 wheels
     "pycocotools>=2.0.7",
-    # scikit-image 0.19+ required for Python 3.10 wheels
-    "scikit-image>=0.19",
-    # scipy 1.7.2+ required for Python 3.10 wheels
-    "scipy>=1.7.2",
+    # scikit-image 0.20+ required for Python 3.11 wheels
+    "scikit-image>=0.20",
+    # scipy 1.9.2+ required for Python 3.11 wheels
+    "scipy>=1.9.2",
 ]
 docs = [
     # ipywidgets 7+ required by nbsphinx

requirements/min-reqs.old

@@ -3,32 +3,32 @@ setuptools==61.0.0
 
 # install
 einops==0.3.0
-fiona==1.8.21
+fiona==1.8.22
 kornia==0.7.4
 lightly==1.4.5
 lightning[pytorch-extra]==2.0.0
-matplotlib==3.5.0
-numpy==1.21.2
-pandas==1.3.3
-pillow==8.4.0
-pyproj==3.3.0
-rasterio==1.3.0.post1
-rtree==1.0.0
+matplotlib==3.6.0
+numpy==1.23.2
+pandas==1.5.0
+pillow==9.2.0
+pyproj==3.4.0
+rasterio==1.3.3
+rtree==1.0.1
 segmentation-models-pytorch==0.2.0
-shapely==1.8.0
+shapely==1.8.5
 timm==0.4.12
-torch==1.13.0
+torch==2.0.0
 torchmetrics==0.10.0
-torchvision==0.14.0
+torchvision==0.15.1
 
 # datasets
-h5py==3.6.0
+h5py==3.8.0
 laspy==2.0.0
-opencv-python==4.5.4.58
+opencv-python==4.5.5.64
 pycocotools==2.0.7
 pyarrow==15.0.0  # Remove when we upgrade min version of pandas to `pandas[parquet]>=2`
-scikit-image==0.19.0
-scipy==1.7.2
+scikit-image==0.20.0
+scipy==1.9.2
 
 # tests
 pytest==7.3.0

tests/datasets/test_advance.py

@@ -12,7 +12,7 @@
 
 from torchgeo.datasets import ADVANCE, DatasetNotFoundError
 
-pytest.importorskip('scipy', minversion='1.7.2')
+pytest.importorskip('scipy', minversion='1.9.2')
 
 
 class TestADVANCE:

tests/datasets/test_cabuar.py

@@ -14,7 +14,7 @@
 
 from torchgeo.datasets import CaBuAr, DatasetNotFoundError
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestCaBuAr:

tests/datasets/test_chabud.py

@@ -13,7 +13,7 @@
 
 from torchgeo.datasets import ChaBuD, DatasetNotFoundError
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestChaBuD:

tests/datasets/test_cropharvest.py

@@ -13,7 +13,7 @@
 
 from torchgeo.datasets import CropHarvest, DatasetNotFoundError
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestCropHarvest:

tests/datasets/test_digital_typhoon.py

@@ -14,7 +14,7 @@
 
 from torchgeo.datasets import DatasetNotFoundError, DigitalTyphoon
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestDigitalTyphoon:

tests/datasets/test_landcoverai.py

@@ -72,7 +72,7 @@ def test_plot(self, dataset: LandCoverAIGeo) -> None:
 
 
 class TestLandCoverAI:
-    pytest.importorskip('cv2', minversion='4.5.4')
+    pytest.importorskip('cv2', minversion='4.5.5')
 
     @pytest.fixture(
         params=product([LandCoverAI100, LandCoverAI], ['train', 'val', 'test'])

tests/datasets/test_mmearth.py

@@ -12,7 +12,7 @@
 
 from torchgeo.datasets import DatasetNotFoundError, MMEarth
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 data_dir_dict = {
     'MMEarth': os.path.join('tests', 'data', 'mmearth', 'data_1M_v001'),

tests/datasets/test_quakeset.py

@@ -13,7 +13,7 @@
 
 from torchgeo.datasets import DatasetNotFoundError, QuakeSet
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestQuakeSet:

tests/datasets/test_skippd.py

@@ -15,7 +15,7 @@
 
 from torchgeo.datasets import SKIPPD, DatasetNotFoundError
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestSKIPPD:

tests/datasets/test_so2sat.py

@@ -13,7 +13,7 @@
 
 from torchgeo.datasets import DatasetNotFoundError, RGBBandsMissingError, So2Sat
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestSo2Sat:
@@ -43,9 +43,7 @@ def test_len(self, dataset: So2Sat) -> None:
         assert len(dataset) == 2
 
     def test_out_of_bounds(self, dataset: So2Sat) -> None:
-        # h5py at version 2.10.0 raises a ValueError instead of an IndexError so we
-        # check for both here
-        with pytest.raises((IndexError, ValueError)):
+        with pytest.raises(IndexError):
             dataset[2]
 
     def test_invalid_split(self) -> None:

tests/datasets/test_vhr10.py

@@ -72,7 +72,7 @@ def test_not_downloaded(self, tmp_path: Path) -> None:
             VHR10(tmp_path)
 
     def test_plot(self, dataset: VHR10) -> None:
-        pytest.importorskip('skimage', minversion='0.19')
+        pytest.importorskip('skimage', minversion='0.20')
         x = dataset[1].copy()
         dataset.plot(x, suptitle='Test')
         plt.close()

tests/datasets/test_zuericrop.py

@@ -12,7 +12,7 @@
 
 from torchgeo.datasets import DatasetNotFoundError, RGBBandsMissingError, ZueriCrop
 
-pytest.importorskip('h5py', minversion='3.6')
+pytest.importorskip('h5py', minversion='3.8')
 
 
 class TestZueriCrop:

torchgeo/datasets/cyclone.py

@@ -131,11 +131,7 @@ def _load_image(self, image_id: str) -> Tensor:
         filename = os.path.join(self.root, self.split, f'{image_id}.jpg')
         with Image.open(filename) as img:
             if img.height != self.size or img.width != self.size:
-                # Moved in PIL 9.1.0
-                try:
-                    resample = Image.Resampling.BILINEAR
-                except AttributeError:
-                    resample = Image.BILINEAR  # type: ignore[attr-defined]
+                resample = Image.Resampling.BILINEAR
                 img = img.resize(size=(self.size, self.size), resample=resample)
             array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array)

torchgeo/datasets/satlas.py

@@ -639,12 +639,6 @@ def _load_image(
             row: Web Mercator row.
             directories: Directories that may contain the image.
         """
-        # Moved in PIL 9.1.0
-        try:
-            resample = Image.Resampling.BILINEAR
-        except AttributeError:
-            resample = Image.BILINEAR  # type: ignore[attr-defined]
-
         # Find directories that match image product
         good_directories: list[str] = []
         for directory in directories:
@@ -659,6 +653,7 @@ def _load_image(
         sample[f'time_{image}'] = torch.tensor(time)
 
         # Load all bands
+        resample = Image.Resampling.BILINEAR
         channels = []
         for band in self.bands[image]:
             path = os.path.join(self.root, image, directory, band, f'{col}_{row}.png')

torchgeo/datasets/seco.py

@@ -171,11 +171,7 @@ def _load_patch(self, root: Path, subdir: Path) -> Tensor:
                     # slowdown here from converting to/from a PIL Image just to resize.
                     # https://gist.github.com/calebrob6/748045ac8d844154067b2eefa47de92f
                     pil_image = Image.fromarray(band_data)
-                    # Moved in PIL 9.1.0
-                    try:
-                        resample = Image.Resampling.BILINEAR
-                    except AttributeError:
-                        resample = Image.BILINEAR  # type: ignore[attr-defined]
+                    resample = Image.Resampling.BILINEAR
                     band_data = np.array(
                         pil_image.resize((264, 264), resample=resample)
                     )

torchgeo/trainers/moco.py

@@ -23,6 +23,7 @@
 from torch.optim.lr_scheduler import (
     CosineAnnealingLR,
     LinearLR,
+    LRScheduler,
     MultiStepLR,
     SequentialLR,
 )
@@ -34,11 +35,6 @@
 from . import utils
 from .base import BaseTask
 
-try:
-    from torch.optim.lr_scheduler import LRScheduler
-except ImportError:
-    from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
-
 
 def moco_augmentations(
     version: int, size: int, weights: Tensor