Bump ruff from 0.4.4 to 0.4.5 in /requirements (microsoft#2080)

* Bump ruff from 0.4.4 to 0.4.5 in /requirements Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.4 to 0.4.5. - [Release notes](https://github.com/astral-sh/ruff/releases) - [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md) - [Commits](astral-sh/ruff@v0.4.4...v0.4.5) --- updated-dependencies: - dependency-name: ruff dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <[email protected]> * min version of numpy now supports type hints * pre-commit: update ruff --------- Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Adam J. Stewart <[email protected]>
yichiac · May 23, 2024 · 5cfbd65 · 5cfbd65
1 parent 2aecee9
commit 5cfbd65
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Showing 35 changed files with 52 additions and 52 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.3.7
+    rev: v0.4.5
     hooks:
       - id: ruff
         types_or:

diff --git a/experiments/ssl4eo/landsat/chip_landsat_benchmark.py b/experiments/ssl4eo/landsat/chip_landsat_benchmark.py
@@ -29,7 +29,7 @@ def retrieve_mask_chip(
         mask array
     """
     out_shape = (1, *img_src.shape)
-    mask_chip: 'np.typing.NDArray[np.uint8]' = mask_src.read(
+    mask_chip: np.typing.NDArray[np.uint8] = mask_src.read(
         out_shape=out_shape, window=from_bounds(*img_src.bounds, mask_src.transform)
     )
 

diff --git a/requirements/style.txt b/requirements/style.txt
@@ -1,3 +1,3 @@
 # style
 mypy==1.10.0
-ruff==0.4.4
+ruff==0.4.5
diff --git a/tests/datasets/test_utils.py b/tests/datasets/test_utils.py
@@ -561,7 +561,7 @@ def test_nonexisting_directory(tmp_path: Path) -> None:
 
 
 def test_percentile_normalization() -> None:
-    img: 'np.typing.NDArray[np.int_]' = np.array([[1, 2], [98, 100]])
+    img: np.typing.NDArray[np.int_] = np.array([[1, 2], [98, 100]])
 
     img = percentile_normalization(img, 2, 98)
     assert img.min() == 0
@@ -573,7 +573,7 @@ def test_percentile_normalization() -> None:
     [np.uint8, np.uint16, np.uint32, np.int8, np.int16, np.int32, np.int64],
 )
 def test_array_to_tensor(array_dtype: 'np.typing.DTypeLike') -> None:
-    array: 'np.typing.NDArray[Any]' = np.zeros((2,), dtype=array_dtype)
+    array: np.typing.NDArray[Any] = np.zeros((2,), dtype=array_dtype)
     array[0] = np.iinfo(array.dtype).min
     array[1] = np.iinfo(array.dtype).max
     tensor = array_to_tensor(array)

diff --git a/torchgeo/datasets/advance.py b/torchgeo/datasets/advance.py
@@ -179,7 +179,7 @@ def _load_image(self, path: str) -> Tensor:
             the image
         """
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/bigearthnet.py b/torchgeo/datasets/bigearthnet.py
@@ -403,7 +403,7 @@ def _load_image(self, index: int) -> Tensor:
                     resampling=Resampling.bilinear,
                 )
                 images.append(array)
-        arrays: 'np.typing.NDArray[np.int_]' = np.stack(images, axis=0)
+        arrays: np.typing.NDArray[np.int_] = np.stack(images, axis=0)
         tensor = torch.from_numpy(arrays).float()
         return tensor
 

diff --git a/torchgeo/datasets/biomassters.py b/torchgeo/datasets/biomassters.py
@@ -196,7 +196,7 @@ def _load_target(self, filename: str) -> Tensor:
             target mask
         """
         with rasterio.open(os.path.join(self.root, 'train_agbm', filename), 'r') as src:
-            arr: 'np.typing.NDArray[np.float_]' = src.read()
+            arr: np.typing.NDArray[np.float_] = src.read()
 
         target = torch.from_numpy(arr).float()
         return target

diff --git a/torchgeo/datasets/cloud_cover.py b/torchgeo/datasets/cloud_cover.py
@@ -183,7 +183,7 @@ def _load_image(self, index: int) -> Tensor:
             with rasterio.open(path) as image_data:
                 image_array = image_data.read(1).astype(np.int32)
                 images.append(image_array)
-        image_stack: 'np.typing.NDArray[np.int_]' = np.stack(images, axis=0)
+        image_stack: np.typing.NDArray[np.int_] = np.stack(images, axis=0)
         image_tensor = torch.from_numpy(image_stack)
         return image_tensor
 
@@ -200,7 +200,7 @@ def _load_target(self, index: int) -> Tensor:
         with rasterio.open(label_asset_path) as target_data:
             target_img = target_data.read(1).astype(np.int32)
 
-        target_array: 'np.typing.NDArray[np.int_]' = np.array(target_img)
+        target_array: np.typing.NDArray[np.int_] = np.array(target_img)
         target_tensor = torch.from_numpy(target_array)
         return target_tensor
 

diff --git a/torchgeo/datasets/cowc.py b/torchgeo/datasets/cowc.py
@@ -145,7 +145,7 @@ def _load_image(self, index: int) -> Tensor:
         """
         filename = os.path.join(self.root, self.images[index])
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img)
+            array: np.typing.NDArray[np.int_] = np.array(img)
             tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/cv4a_kenya_crop_type.py b/torchgeo/datasets/cv4a_kenya_crop_type.py
@@ -235,7 +235,7 @@ def _load_label_tile(self, tile_name: str) -> tuple[Tensor, Tensor]:
         )
 
         with Image.open(os.path.join(directory, 'labels.tif')) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img)
+            array: np.typing.NDArray[np.int_] = np.array(img)
             labels = torch.from_numpy(array)
 
         with Image.open(os.path.join(directory, 'field_ids.tif')) as img:
@@ -332,7 +332,7 @@ def _load_single_image_tile(
                 f'{band_name}.tif',
             )
             with Image.open(filepath) as band_img:
-                array: 'np.typing.NDArray[np.int_]' = np.array(band_img)
+                array: np.typing.NDArray[np.int_] = np.array(band_img)
                 img[band_index] = torch.from_numpy(array)
 
         return img

diff --git a/torchgeo/datasets/cyclone.py b/torchgeo/datasets/cyclone.py
@@ -159,7 +159,7 @@ def _load_image(self, directory: str) -> Tensor:
                 except AttributeError:
                     resample = Image.BILINEAR  # type: ignore[attr-defined]
                 img = img.resize(size=(self.size, self.size), resample=resample)
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array)
             tensor = tensor.permute((2, 0, 1)).float()
             return tensor

diff --git a/torchgeo/datasets/deepglobelandcover.py b/torchgeo/datasets/deepglobelandcover.py
@@ -184,7 +184,7 @@ def _load_image(self, index: int) -> Tensor:
         path = self.image_fns[index]
 
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img)
+            array: np.typing.NDArray[np.int_] = np.array(img)
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1)).to(torch.float32)
@@ -201,7 +201,7 @@ def _load_target(self, index: int) -> Tensor:
         """
         path = self.mask_fns[index]
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.uint8]' = np.array(img)
+            array: np.typing.NDArray[np.uint8] = np.array(img)
             array = rgb_to_mask(array, self.colormap)
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW

diff --git a/torchgeo/datasets/dfc2022.py b/torchgeo/datasets/dfc2022.py
@@ -235,7 +235,7 @@ def _load_image(self, path: str, shape: Sequence[int] | None = None) -> Tensor:
             the image
         """
         with rasterio.open(path) as f:
-            array: 'np.typing.NDArray[np.float_]' = f.read(
+            array: np.typing.NDArray[np.float_] = f.read(
                 out_shape=shape, out_dtype='float32', resampling=Resampling.bilinear
             )
             tensor = torch.from_numpy(array)
@@ -251,7 +251,7 @@ def _load_target(self, path: str) -> Tensor:
             the target mask
         """
         with rasterio.open(path) as f:
-            array: 'np.typing.NDArray[np.int_]' = f.read(
+            array: np.typing.NDArray[np.int_] = f.read(
                 indexes=1, out_dtype='int32', resampling=Resampling.bilinear
             )
             tensor = torch.from_numpy(array)

diff --git a/torchgeo/datasets/etci2021.py b/torchgeo/datasets/etci2021.py
@@ -204,7 +204,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
@@ -221,7 +221,7 @@ def _load_target(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('L'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('L'))
             tensor = torch.from_numpy(array)
             tensor = torch.clamp(tensor, min=0, max=1)
             tensor = tensor.to(torch.long)

diff --git a/torchgeo/datasets/eurocrops.py b/torchgeo/datasets/eurocrops.py
@@ -246,7 +246,7 @@ def plot(
         def apply_cmap(arr: 'np.typing.NDArray[Any]') -> 'np.typing.NDArray[np.float_]':
             # Color 0 as black, while applying default color map for the class indices.
             cmap = plt.get_cmap('viridis')
-            im: 'np.typing.NDArray[np.float_]' = cmap(arr / len(self.class_map))
+            im: np.typing.NDArray[np.float_] = cmap(arr / len(self.class_map))
             im[arr == 0] = 0
             return im
 

diff --git a/torchgeo/datasets/fair1m.py b/torchgeo/datasets/fair1m.py
@@ -308,7 +308,7 @@ def _load_image(self, path: str) -> Tensor:
             the image
         """
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/forestdamage.py b/torchgeo/datasets/forestdamage.py
@@ -197,7 +197,7 @@ def _load_image(self, path: str) -> Tensor:
             the image
         """
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor: Tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/geo.py b/torchgeo/datasets/geo.py
@@ -904,7 +904,7 @@ def _load_image(self, index: int) -> tuple[Tensor, Tensor]:
             the image and class label
         """
         img, label = ImageFolder.__getitem__(self, index)
-        array: 'np.typing.NDArray[np.int_]' = np.array(img)
+        array: np.typing.NDArray[np.int_] = np.array(img)
         tensor = torch.from_numpy(array).float()
         # Convert from HxWxC to CxHxW
         tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/gid15.py b/torchgeo/datasets/gid15.py
@@ -189,7 +189,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1)).float()
@@ -206,7 +206,7 @@ def _load_target(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('L'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('L'))
             tensor = torch.from_numpy(array)
             tensor = tensor.to(torch.long)
             return tensor

diff --git a/torchgeo/datasets/idtrees.py b/torchgeo/datasets/idtrees.py
@@ -265,7 +265,7 @@ def _load_las(self, path: str) -> Tensor:
         """
         laspy = lazy_import('laspy')
         las = laspy.read(path)
-        array: 'np.typing.NDArray[np.int_]' = np.stack([las.x, las.y, las.z], axis=0)
+        array: np.typing.NDArray[np.int_] = np.stack([las.x, las.y, las.z], axis=0)
         tensor = torch.from_numpy(array)
         return tensor
 
@@ -570,7 +570,7 @@ def plot_las(self, index: int) -> 'pyvista.Plotter':  # type: ignore[name-define
         path = self.images[index]
         path = path.replace('RGB', 'LAS').replace('.tif', '.las')
         las = laspy.read(path)
-        points: 'np.typing.NDArray[np.int_]' = np.stack(
+        points: np.typing.NDArray[np.int_] = np.stack(
             [las.x, las.y, las.z], axis=0
         ).transpose((1, 0))
         point_cloud = pyvista.PolyData(points)

diff --git a/torchgeo/datasets/landcoverai.py b/torchgeo/datasets/landcoverai.py
@@ -360,7 +360,7 @@ def _load_image(self, id_: str) -> Tensor:
         """
         filename = os.path.join(self.root, 'output', id_ + '.jpg')
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img)
+            array: np.typing.NDArray[np.int_] = np.array(img)
             tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
@@ -378,7 +378,7 @@ def _load_target(self, id_: str) -> Tensor:
         """
         filename = os.path.join(self.root, 'output', id_ + '_m.png')
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('L'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('L'))
             tensor = torch.from_numpy(array).long()
             return tensor
 

diff --git a/torchgeo/datasets/levircd.py b/torchgeo/datasets/levircd.py
@@ -105,7 +105,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
@@ -122,7 +122,7 @@ def _load_target(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('L'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('L'))
             tensor = torch.from_numpy(array)
             tensor = torch.clamp(tensor, min=0, max=1)
             tensor = tensor.to(torch.long)

diff --git a/torchgeo/datasets/loveda.py b/torchgeo/datasets/loveda.py
@@ -208,7 +208,7 @@ def _load_image(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor = torch.from_numpy(array).float()
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))
@@ -225,7 +225,7 @@ def _load_target(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('L'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('L'))
             tensor = torch.from_numpy(array)
             tensor = tensor.to(torch.long)
             return tensor

diff --git a/torchgeo/datasets/mapinwild.py b/torchgeo/datasets/mapinwild.py
@@ -217,7 +217,7 @@ def _load_raster(self, filename: int, source: str) -> Tensor:
         """
         with rasterio.open(os.path.join(self.root, source, f'{filename}.tif')) as f:
             raw_array = f.read()
-            array: 'np.typing.NDArray[np.int_]' = np.stack(raw_array, axis=0)
+            array: np.typing.NDArray[np.int_] = np.stack(raw_array, axis=0)
             if array.dtype == np.uint16:
                 array = array.astype(np.int32)
             tensor = torch.from_numpy(array).float()

diff --git a/torchgeo/datasets/millionaid.py b/torchgeo/datasets/millionaid.py
@@ -305,7 +305,7 @@ def _load_image(self, path: str) -> Tensor:
             the image
         """
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('RGB'))
             tensor: Tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/oscd.py b/torchgeo/datasets/oscd.py
@@ -216,11 +216,11 @@ def _load_image(self, paths: Sequence[str]) -> Tensor:
         Returns:
             the image
         """
-        images: list['np.typing.NDArray[np.int_]'] = []
+        images: list[np.typing.NDArray[np.int_]] = []
         for path in paths:
             with Image.open(path) as img:
                 images.append(np.array(img))
-        array: 'np.typing.NDArray[np.int_]' = np.stack(images, axis=0).astype(np.int_)
+        array: np.typing.NDArray[np.int_] = np.stack(images, axis=0).astype(np.int_)
         tensor = torch.from_numpy(array).float()
         return tensor
 
@@ -235,7 +235,7 @@ def _load_target(self, path: str) -> Tensor:
         """
         filename = os.path.join(path)
         with Image.open(filename) as img:
-            array: 'np.typing.NDArray[np.int_]' = np.array(img.convert('L'))
+            array: np.typing.NDArray[np.int_] = np.array(img.convert('L'))
             tensor = torch.from_numpy(array)
             tensor = torch.clamp(tensor, min=0, max=1)
             tensor = tensor.to(torch.long)
@@ -314,7 +314,7 @@ def get_masked(img: Tensor) -> 'np.typing.NDArray[np.uint8]':
             rgb_img = (np.clip((rgb_img - per02) / (per98 - per02), 0, 1) * 255).astype(
                 np.uint8
             )
-            array: 'np.typing.NDArray[np.uint8]' = draw_semantic_segmentation_masks(
+            array: np.typing.NDArray[np.uint8] = draw_semantic_segmentation_masks(
                 torch.from_numpy(rgb_img),
                 sample['mask'],
                 alpha=alpha,

diff --git a/torchgeo/datasets/potsdam.py b/torchgeo/datasets/potsdam.py
@@ -206,7 +206,7 @@ def _load_target(self, index: int) -> Tensor:
         """
         path = self.files[index]['mask']
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.uint8]' = np.array(img.convert('RGB'))
+            array: np.typing.NDArray[np.uint8] = np.array(img.convert('RGB'))
             array = rgb_to_mask(array, self.colormap)
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW

diff --git a/torchgeo/datasets/reforestree.py b/torchgeo/datasets/reforestree.py
@@ -147,7 +147,7 @@ def _load_image(self, path: str) -> Tensor:
             the image
         """
         with Image.open(path) as img:
-            array: 'np.typing.NDArray[np.uint8]' = np.array(img)
+            array: np.typing.NDArray[np.uint8] = np.array(img)
             tensor = torch.from_numpy(array)
             # Convert from HxWxC to CxHxW
             tensor = tensor.permute((2, 0, 1))

diff --git a/torchgeo/datasets/so2sat.py b/torchgeo/datasets/so2sat.py
@@ -230,7 +230,7 @@ def __init__(
         assert split in self.filenames_by_version[version]
 
         self._validate_bands(bands)
-        self.s1_band_indices: 'np.typing.NDArray[np.int_]' = np.array(
+        self.s1_band_indices: np.typing.NDArray[np.int_] = np.array(
             [
                 self.all_s1_band_names.index(b)
                 for b in bands
@@ -240,7 +240,7 @@ def __init__(
 
         self.s1_band_names = [self.all_s1_band_names[i] for i in self.s1_band_indices]
 
-        self.s2_band_indices: 'np.typing.NDArray[np.int_]' = np.array(
+        self.s2_band_indices: np.typing.NDArray[np.int_] = np.array(
             [
                 self.all_s2_band_names.index(b)
                 for b in bands

diff --git a/torchgeo/datasets/spacenet.py b/torchgeo/datasets/spacenet.py
@@ -686,7 +686,7 @@ def _load_mask(
         max_speed_bin = 65
         speed_arr_bin = np.arange(min_speed_bin, max_speed_bin + 1)
         bin_size_mph = 10.0
-        speed_cls_arr: 'np.typing.NDArray[np.int_]' = np.array(
+        speed_cls_arr: np.typing.NDArray[np.int_] = np.array(
             [math.ceil(s / bin_size_mph) for s in speed_arr_bin]
         )