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torchgeo
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Bump black[jupyter] from 22.12.0 to 23.1.0 in /requirements (#1080) 

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

Bumps [black[jupyter]](https://github.com/psf/black) from 22.12.0 to 23.1.0.
- [Release notes](https://github.com/psf/black/releases)
- [Changelog](https://github.com/psf/black/blob/main/CHANGES.md)
- [Commits](https://github.com/psf/black/compare/22.12.0...23.1.0)

---
updated-dependencies:
- dependency-name: black[jupyter]
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

* Style changes

* Resolve discrepancy between black and pydocstyle

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
This commit is contained in:
dependabot[bot] 2023-02-01 22:26:54 +00:00 коммит произвёл GitHub
Родитель 50a4068e18
Коммит 2fe0c1cf3e
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Идентификатор ключа GPG: 4AEE18F83AFDEB23
28 изменённых файлов: 16 добавлений и 49 удалений
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2
.pre-commit-config.yaml
Просмотреть файл

@ -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]

1
evaluate.py
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@ -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)

4
experiments/run_chesapeake_cvpr_experiments.py
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@ -36,7 +36,7 @@ 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,
@ -44,7 +44,6 @@ if __name__ == "__main__":
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")
@ -52,7 +51,6 @@ if __name__ == "__main__":
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}"

2
experiments/run_cowc_experiments.py
Просмотреть файл

@ -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}"

2
experiments/run_cowc_seed_experiments.py
Просмотреть файл

@ -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}"

4
experiments/run_landcoverai_experiments.py
Просмотреть файл

@ -35,10 +35,9 @@ 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")
@ -46,7 +45,6 @@ if __name__ == "__main__":
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}"

4
experiments/run_landcoverai_seed_experiments.py
Просмотреть файл

@ -35,10 +35,9 @@ 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")
@ -46,7 +45,6 @@ if __name__ == "__main__":
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}"

4
experiments/run_resisc45_experiments.py
Просмотреть файл

@ -34,10 +34,9 @@ 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")
@ -45,7 +44,6 @@ if __name__ == "__main__":
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}"

4
experiments/run_so2sat_byol_experiments.py
Просмотреть файл

@ -36,10 +36,9 @@ 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")
@ -47,7 +46,6 @@ if __name__ == "__main__":
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}"

4
experiments/run_so2sat_experiments.py
Просмотреть файл

@ -34,10 +34,9 @@ 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")
@ -45,7 +44,6 @@ if __name__ == "__main__":
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}"

4
experiments/run_so2sat_seed_experiments.py
Просмотреть файл

@ -35,10 +35,9 @@ 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")
@ -46,7 +45,6 @@ if __name__ == "__main__":
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}"

3
experiments/test_chesapeakecvpr_models.py
Просмотреть файл

@ -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"],

2
requirements/style.txt
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@ -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

2
setup.cfg
Просмотреть файл

@ -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

1
tests/data/cdl/data.py
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@ -54,7 +54,6 @@ raster_extensions = [".tif", ".tif.ovr"]
if __name__ == "__main__":
for dir in directories:
filename = dir + ".zip"

2
tests/data/enviroatlas/data.py
Просмотреть файл

@ -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))

1
tests/data/globbiomass/data.py
Просмотреть файл

@ -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 = []

1
tests/data/inria/data.py
Просмотреть файл

@ -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)

3
tests/data/ref_cloud_cover_detection_challenge_v1/data.py
Просмотреть файл

@ -96,7 +96,6 @@ TEST_EXTENT = Extent(
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(

1
tests/data/spacenet/data.py
Просмотреть файл

@ -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)

1
tests/datasets/test_agb_live_woody_density.py
Просмотреть файл

@ -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

1
tests/datasets/test_eudem.py
Просмотреть файл

@ -18,7 +18,6 @@ from torchgeo.datasets import EUDEM, BoundingBox, IntersectionDataset, UnionData
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")

1
tests/datasets/test_inria.py
Просмотреть файл

@ -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)

1
tests/datasets/test_openbuildings.py
Просмотреть файл

@ -29,7 +29,6 @@ pd = pytest.importorskip("pandas", minversion="0.23.2")
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

1
tests/datasets/test_usavars.py
Просмотреть файл

@ -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"

4
torchgeo/datasets/chesapeake.py
Просмотреть файл

@ -650,7 +650,6 @@ class ChesapeakeCVPR(GeoDataset):
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 @@ class ChesapeakeCVPR(GeoDataset):
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

4
torchgeo/datasets/enviroatlas.py
Просмотреть файл

@ -359,7 +359,6 @@ class EnviroAtlas(GeoDataset):
query_box = shapely.geometry.box(minx, miny, maxx, maxy)
for layer in self.layers:
fn = filenames[layer]
with rasterio.open(
@ -416,10 +415,11 @@ class EnviroAtlas(GeoDataset):
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

1
torchgeo/samplers/batch.py
Просмотреть файл

@ -144,7 +144,6 @@ class RandomBatchGeoSampler(BatchGeoSampler):
# 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)