Bump black[jupyter] from 22.12.0 to 23.1.0 in /requirements

.pre-commit-config.yaml

@@ -12,7 +12,7 @@ repos:
              additional_dependencies: ["colorama>=0.4.3"]
 
     -   repo: https://github.com/psf/black
-        rev: 22.12.0
+        rev: 23.1.0
         hooks:
         -   id: black
             args: [--skip-magic-trailing-comma]

evaluate.py

@@ -213,7 +213,6 @@ def main(args: argparse.Namespace) -> None:
     model = model.to(device)
 
     if args.task == "etci2021":  # Custom metric setup for testing ETCI2021
-
         metrics = MetricCollection([BinaryAccuracy(), BinaryJaccardIndex()]).to(device)
 
         val_results = run_eval_loop(model, dm.val_dataloader(), device, metrics)

experiments/run_chesapeake_cvpr_experiments.py

@@ -36,23 +36,21 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (train_state, model, backbone, lr, loss, weight_init) in itertools.product(
+    for train_state, model, backbone, lr, loss, weight_init in itertools.product(
         training_set_options,
         model_options,
         backbone_options,
         lr_options,
         loss_options,
         weight_init_options,
     ):
-
         experiment_name = f"{train_state}_{model}_{backbone}_{lr}_{loss}_{weight_init}"
 
         output_dir = os.path.join("output", "chesapeake-cvpr_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "chesapeake_cvpr.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/run_cowc_experiments.py

@@ -35,7 +35,7 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, lr, pretrained) in itertools.product(
+    for model, lr, pretrained in itertools.product(
         model_options, lr_options, pretrained_options
     ):
         experiment_name = f"{model}_{lr}_{pretrained}"

experiments/run_cowc_seed_experiments.py

@@ -36,7 +36,7 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, lr, pretrained, seed) in itertools.product(
+    for model, lr, pretrained, seed in itertools.product(
         model_options, lr_options, pretrained_options, seeds
     ):
         experiment_name = f"{model}_{lr}_{pretrained}_{seed}"

experiments/run_landcoverai_experiments.py

@@ -35,18 +35,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, backbone, lr, loss, weight_init) in itertools.product(
+    for model, backbone, lr, loss, weight_init in itertools.product(
         model_options, backbone_options, lr_options, loss_options, weight_init_options
     ):
-
         experiment_name = f"{model}_{backbone}_{lr}_{loss}_{weight_init}"
 
         output_dir = os.path.join("output", "landcoverai_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "landcoverai.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/run_landcoverai_seed_experiments.py

@@ -35,18 +35,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, backbone, lr, loss, weight_init) in itertools.product(
+    for model, backbone, lr, loss, weight_init in itertools.product(
         model_options, backbone_options, lr_options, loss_options, weight_init_options
     ):
-
         experiment_name = f"{model}_{backbone}_{lr}_{loss}_{weight_init}"
 
         output_dir = os.path.join("output", "landcoverai_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "landcoverai.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/run_resisc45_experiments.py

@@ -34,18 +34,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, lr, loss, weights) in itertools.product(
+    for model, lr, loss, weights in itertools.product(
         model_options, lr_options, loss_options, weight_options
     ):
-
         experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_','-')}"
 
         output_dir = os.path.join("output", "resisc45_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "resisc45.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/run_so2sat_byol_experiments.py

@@ -36,18 +36,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, lr, loss, weights, bands) in itertools.product(
+    for model, lr, loss, weights, bands in itertools.product(
         model_options, lr_options, loss_options, weight_options, bands_options
     ):
-
         experiment_name = f"{model}_{lr}_{loss}_byol_{bands}-{weights.split('/')[-2]}"
 
         output_dir = os.path.join("output", "so2sat_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "so2sat.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/run_so2sat_experiments.py

@@ -34,18 +34,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, lr, loss, weights) in itertools.product(
+    for model, lr, loss, weights in itertools.product(
         model_options, lr_options, loss_options, weight_options
     ):
-
         experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_','-')}"
 
         output_dir = os.path.join("output", "so2sat_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "so2sat.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/run_so2sat_seed_experiments.py

@@ -35,18 +35,16 @@ def do_work(work: "Queue[str]", gpu_idx: int) -> bool:
 if __name__ == "__main__":
     work: "Queue[str]" = Queue()
 
-    for (model, lr, loss, weights, seed) in itertools.product(
+    for model, lr, loss, weights, seed in itertools.product(
         model_options, lr_options, loss_options, weight_options, seeds
     ):
-
         experiment_name = f"{model}_{lr}_{loss}_{weights.replace('_','-')}_{seed}"
 
         output_dir = os.path.join("output", "so2sat_seed_experiments")
         log_dir = os.path.join(output_dir, "logs")
         config_file = os.path.join("conf", "so2sat.yaml")
 
         if not os.path.exists(os.path.join(output_dir, experiment_name)):
-
             command = (
                 "python train.py"
                 + f" config_file={config_file}"

experiments/test_chesapeakecvpr_models.py

@@ -94,7 +94,6 @@ def main(args: argparse.Namespace) -> None:
     )
 
     for experiment_dir in os.listdir(args.input_dir):
-
         checkpoint_fn = None
         for fn in os.listdir(os.path.join(args.input_dir, experiment_dir)):
             if fn.startswith("epoch") and fn.endswith(".ckpt"):
@@ -109,7 +108,6 @@ def main(args: argparse.Namespace) -> None:
         checkpoint_fn = os.path.join(args.input_dir, experiment_dir, checkpoint_fn)
 
         try:
-
             model = ChesapeakeCVPRSegmentationTask.load_from_checkpoint(checkpoint_fn)
             model.freeze()
             model.eval()
@@ -136,7 +134,6 @@ def main(args: argparse.Namespace) -> None:
 
         # Test the loaded model against the test set from all states
         for test_splits in ALL_TEST_SPLITS:
-
             dm = ChesapeakeCVPRDataModule(
                 root=args.chesapeakecvpr_root,
                 train_splits=["de-train"],

requirements/style.txt

@@ -1,5 +1,5 @@
 # style
-black[jupyter]==22.12.0
+black[jupyter]==23.1.0
 flake8==6.0.0
 isort[colors]==5.12.0
 pydocstyle[toml]==6.3.0

setup.cfg

@@ -109,7 +109,7 @@ docs =
     sphinx>=4,<7
 style =
     # black 21.8+ required for Jupyter support
-    black[jupyter]>=21.8,<23
+    black[jupyter]>=21.8,<24
     # flake8 3.8+ depends on pyflakes 2.2+, which fixes a bug with mypy error code ignores:
     # https://github.com/PyCQA/pyflakes/pull/455
     flake8>=3.8,<7

tests/data/cdl/data.py

@@ -54,7 +54,6 @@ def create_file(path: str, dtype: str, num_channels: int) -> None:
 
 
 if __name__ == "__main__":
-
     for dir in directories:
         filename = dir + ".zip"
 

tests/data/enviroatlas/data.py

@@ -229,7 +229,6 @@ def generate_test_data(root: str) -> str:
 
     for prefix in tile_list:
         for suffix, data_profile in layer_data_profiles.items():
-
             img_path = os.path.join(folder_path, f"{prefix}_{suffix}.tif")
             img_dir = os.path.dirname(img_path)
             if not os.path.exists(img_dir):
@@ -273,7 +272,6 @@ def generate_test_data(root: str) -> str:
         schema=schema,
     ) as dst:
         for prefix in tile_list:
-
             img_path = os.path.join(folder_path, f"{prefix}_a_naip.tif")
             with rasterio.open(img_path) as f:
                 geom = shapely.geometry.mapping(shapely.geometry.box(*f.bounds))

tests/data/globbiomass/data.py

@@ -41,7 +41,6 @@ def create_file(path: str, dtype: str, num_channels: int) -> None:
 
 
 if __name__ == "__main__":
-
     for measurement, file_paths in files.items():
         zipfilename = f"N00E020_{measurement}.zip"
         files_to_zip = []

tests/data/inria/data.py

@@ -63,7 +63,6 @@ def generate_test_data(root: str, n_samples: int = 2) -> str:
         os.makedirs(timg_dir)
 
     for i in range(n_samples):
-
         dtype_max = np.iinfo(dtype).max
         img = np.random.randint(dtype_max, size=size, dtype=dtype)
         lbl = np.random.randint(2, size=size, dtype=dtype)

tests/data/ref_cloud_cover_detection_challenge_v1/data.py

@@ -96,7 +96,6 @@
 
 
 def create_raster(path: str, dtype: str, num_channels: int, collection: str) -> None:
-
     if not os.path.exists(os.path.split(path)[0]):
         Path(os.path.split(path)[0]).mkdir(parents=True)
 
@@ -164,7 +163,6 @@ def get_class_label_list(overview: LabelOverview) -> LabelClasses:
 
 
 def get_item_class_overview(label_type: LabelType, asset_path: str) -> LabelOverview:
-
     """Takes a path to an asset based on type and returns the class label
     overview object
 
@@ -224,7 +222,6 @@ def create_label_item() -> Item:
 
 
 if __name__ == "__main__":
-
     # create a geotiff for each s2 band
     for b in BANDS:
         tif_path = os.path.join(

tests/data/spacenet/data.py

@@ -175,7 +175,6 @@ def main() -> None:
     ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
 
     for dataset in datasets:
-
         collections = list(dataset.collection_md5_dict.keys())
         for collection in collections:
             dataset = cast(SpaceNet, dataset)

tests/datasets/test_agb_live_woody_density.py

@@ -29,7 +29,6 @@ class TestAbovegroundLiveWoodyBiomassDensity:
     def dataset(
         self, monkeypatch: MonkeyPatch, tmp_path: Path
     ) -> AbovegroundLiveWoodyBiomassDensity:
-
         transforms = nn.Identity()
         monkeypatch.setattr(
             torchgeo.datasets.agb_live_woody_density, "download_url", download_url

tests/datasets/test_eudem.py

@@ -18,7 +18,6 @@
 class TestEUDEM:
     @pytest.fixture
     def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> EUDEM:
-
         md5s = {"eu_dem_v11_E30N10.zip": "ef148466c02197a08be169eaad186591"}
         monkeypatch.setattr(EUDEM, "md5s", md5s)
         zipfile = os.path.join("tests", "data", "eudem", "eu_dem_v11_E30N10.zip")

tests/datasets/test_inria.py

@@ -19,7 +19,6 @@ class TestInriaAerialImageLabeling:
     def dataset(
         self, request: SubRequest, monkeypatch: MonkeyPatch
     ) -> InriaAerialImageLabeling:
-
         root = os.path.join("tests", "data", "inria")
         test_md5 = "478688944e4797c097d9387fd0b3f038"
         monkeypatch.setattr(InriaAerialImageLabeling, "md5", test_md5)

tests/datasets/test_openbuildings.py

@@ -29,7 +29,6 @@
 class TestOpenBuildings:
     @pytest.fixture
     def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> OpenBuildings:
-
         root = str(tmp_path)
         shutil.copy(
             os.path.join("tests", "data", "openbuildings", "tiles.geojson"), root

tests/datasets/test_usavars.py

@@ -39,7 +39,6 @@ class TestUSAVars:
     def dataset(
         self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
     ) -> USAVars:
-
         monkeypatch.setattr(torchgeo.datasets.usavars, "download_url", download_url)
 
         md5 = "b504580a00bdc27097d5421dec50481b"

torchgeo/datasets/chesapeake.py

@@ -650,7 +650,6 @@ def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:
             query_box = shapely.geometry.box(minx, miny, maxx, maxy)
 
             for layer in self.layers:
-
                 fn = filenames[layer]
 
                 with rasterio.open(os.path.join(self.root, fn)) as f:
@@ -702,10 +701,11 @@ def _verify(self) -> None:
         Raises:
             RuntimeError: if ``download=False`` but dataset is missing or checksum fails
         """
-        # Check if the extracted files already exist
+
         def exists(filename: str) -> bool:
             return os.path.exists(os.path.join(self.root, filename))
 
+        # Check if the extracted files already exist
         if all(map(exists, self.files)):
             return
 

torchgeo/datasets/enviroatlas.py

@@ -359,7 +359,6 @@ def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:
             query_box = shapely.geometry.box(minx, miny, maxx, maxy)
 
             for layer in self.layers:
-
                 fn = filenames[layer]
 
                 with rasterio.open(
@@ -416,10 +415,11 @@ def _verify(self) -> None:
         Raises:
             RuntimeError: if ``download=False`` but dataset is missing or checksum fails
         """
-        # Check if the extracted files already exist
+
         def exists(filename: str) -> bool:
             return os.path.exists(os.path.join(self.root, "enviroatlas_lotp", filename))
 
+        # Check if the extracted files already exist
         if all(map(exists, self.files)):
             return
 

torchgeo/samplers/batch.py

@@ -144,7 +144,6 @@ def __iter__(self) -> Iterator[List[BoundingBox]]:
             # Choose random indices within that tile
             batch = []
             for _ in range(self.batch_size):
-
                 bounding_box = get_random_bounding_box(bounds, self.size, self.res)
                 batch.append(bounding_box)