Newer black

Author: adamjstewart

experiments/ssl4eo/landsat/chip_landsat_benchmark.py

@@ -65,9 +65,10 @@ def retrieve_mask_chip(
         layer_name = "cdl"
 
     for img_path in tqdm(paths):
-        with rasterio.open(img_path) as img_src, rasterio.open(
-            args.mask_path
-        ) as mask_src:
+        with (
+            rasterio.open(img_path) as img_src,
+            rasterio.open(args.mask_path) as mask_src,
+        ):
             if mask_src.crs != img_src.crs:
                 mask_src = WarpedVRT(mask_src, crs=img_src.crs)
 

torchgeo/datamodules/l7irish.py

@@ -68,11 +68,9 @@ def setup(self, stage: str) -> None:
         """
         dataset = L7Irish(**self.kwargs)
         generator = torch.Generator().manual_seed(0)
-        (
-            self.train_dataset,
-            self.val_dataset,
-            self.test_dataset,
-        ) = random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        (self.train_dataset, self.val_dataset, self.test_dataset) = (
+            random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        )
 
         if stage in ["fit"]:
             self.train_batch_sampler = RandomBatchGeoSampler(

torchgeo/datamodules/l8biome.py

@@ -68,11 +68,9 @@ def setup(self, stage: str) -> None:
         """
         dataset = L8Biome(**self.kwargs)
         generator = torch.Generator().manual_seed(0)
-        (
-            self.train_dataset,
-            self.val_dataset,
-            self.test_dataset,
-        ) = random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        (self.train_dataset, self.val_dataset, self.test_dataset) = (
+            random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator)
+        )
 
         if stage in ["fit"]:
             self.train_batch_sampler = RandomBatchGeoSampler(

torchgeo/datasets/idtrees.py

@@ -406,14 +406,12 @@ def _load_geometries(self, directory: str) -> dict[int, dict[str, Any]]:
     @overload
     def _filter_boxes(
         self, image_size: tuple[int, int], min_size: int, boxes: Tensor, labels: Tensor
-    ) -> tuple[Tensor, Tensor]:
-        ...
+    ) -> tuple[Tensor, Tensor]: ...
 
     @overload
     def _filter_boxes(
         self, image_size: tuple[int, int], min_size: int, boxes: Tensor, labels: None
-    ) -> tuple[Tensor, None]:
-        ...
+    ) -> tuple[Tensor, None]: ...
 
     def _filter_boxes(
         self,

torchgeo/datasets/ssl4eo_benchmark.py

@@ -245,9 +245,11 @@ def _download(self) -> None:
         download_url(
             self.url.format(self.mask_dir_name),
             self.root,
-            md5=self.mask_md5s[self.sensor.split("_")[0]][self.product]
-            if self.checksum
-            else None,
+            md5=(
+                self.mask_md5s[self.sensor.split("_")[0]][self.product]
+                if self.checksum
+                else None
+            ),
         )
 
     def _extract(self) -> None:

torchgeo/models/farseg.py

@@ -219,9 +219,11 @@ def __init__(
                             ),
                             BatchNorm2d(out_channels),
                             ReLU(inplace=True),
-                            UpsamplingBilinear2d(scale_factor=2)
-                            if num_upsample != 0
-                            else Identity(),
+                            (
+                                UpsamplingBilinear2d(scale_factor=2)
+                                if num_upsample != 0
+                                else Identity()
+                            ),
                         )
                         for idx in range(num_layers)
                     ]

torchgeo/samplers/utils.py

@@ -12,13 +12,11 @@
 
 
 @overload
-def _to_tuple(value: Union[tuple[int, int], int]) -> tuple[int, int]:
-    ...
+def _to_tuple(value: Union[tuple[int, int], int]) -> tuple[int, int]: ...
 
 
 @overload
-def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]:
-    ...
+def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]: ...
 
 
 def _to_tuple(value: Union[tuple[float, float], float]) -> tuple[float, float]: