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style: format code with isort and Yapf #31

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merged 1 commit into from
Mar 29, 2024
Merged

style: format code with isort and Yapf #31

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4 changes: 2 additions & 2 deletions hsf/augmentation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,9 @@ def get_augmentation_pipeline(augmentation_cfg: DictConfig) -> tio.Compose:
flip = tio.RandomFlip(**augmentation_cfg.flip)
resample = tio.OneOf({
tio.RandomAffine(**augmentation_cfg.affine):
augmentation_cfg.affine_probability,
augmentation_cfg.affine_probability,
tio.RandomElasticDeformation(**augmentation_cfg.elastic):
augmentation_cfg.elastic_probability
augmentation_cfg.elastic_probability
})

return tio.Compose((flip, resample))
Expand Down
11 changes: 6 additions & 5 deletions hsf/factory.py
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Expand Up @@ -28,7 +28,6 @@
log = logging.getLogger(__name__)



def get_lr_hippocampi(mri: PosixPath, cfg: DictConfig) -> tuple:
"""
Get left and right hippocampi from a given MRI.
Expand Down Expand Up @@ -63,8 +62,8 @@ def get_lr_hippocampi(mri: PosixPath, cfg: DictConfig) -> tuple:
original_mri_path=mri)


def predict(mri: PosixPath, second_mri: Optional[PosixPath], engines: Generator,
cfg: DictConfig) -> tuple:
def predict(mri: PosixPath, second_mri: Optional[PosixPath],
engines: Generator, cfg: DictConfig) -> tuple:
"""
Predict the hippocampal segmentation for a given MRI.

Expand Down Expand Up @@ -156,7 +155,8 @@ def filter_mris(mris: List[PosixPath], overwrite: bool) -> List[PosixPath]:
def _get_segmentations(mri: PosixPath) -> List[PosixPath]:
extensions = "".join(mri.suffixes)
stem = mri.name.replace(extensions, "")
segmentations = list(mri.parent.glob(f"{stem}*_hippocampus_seg.nii.gz"))
segmentations = list(
mri.parent.glob(f"{stem}*_hippocampus_seg.nii.gz"))

if len(segmentations) > 2:
log.warning(
Expand Down Expand Up @@ -229,7 +229,8 @@ def main(cfg: DictConfig) -> None:
else:
additional_hippocampi = [None, None]

for j, hippocampus in enumerate(zip(hippocampi, additional_hippocampi)):
for j, hippocampus in enumerate(zip(hippocampi,
additional_hippocampi)):
engines = get_inference_engines(
cfg.segmentation.models_path,
engine_name=cfg.hardware.engine,
Expand Down
3 changes: 1 addition & 2 deletions hsf/multispectrality.py
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Expand Up @@ -78,8 +78,7 @@ def register(mri: PosixPath,
transformlist=transformation["fwdtransforms"])

extensions = "".join(second_contrast.suffixes)
fname = second_contrast.name.replace(extensions,
"") + "_registered.nii.gz"
fname = second_contrast.name.replace(extensions, "") + "_registered.nii.gz"
output_dir = mri.parent / cfg.files.output_dir
output_dir.mkdir(parents=True, exist_ok=True)

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3 changes: 2 additions & 1 deletion hsf/roiloc_wrapper.py
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Expand Up @@ -50,7 +50,8 @@ def get_mri(mri: PosixPath, mask_pattern: Optional[str] = None) -> tuple:
else:
mask = None
log.warning(
"Couldn't find brain extraction mask for the provided pattern.")
"Couldn't find brain extraction mask for the provided pattern."
)
else:
mask = None

Expand Down
7 changes: 3 additions & 4 deletions hsf/segment.py
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Expand Up @@ -67,14 +67,13 @@ def to_ca_mode(logits: torch.Tensor, ca_mode: str = "1/2/3") -> torch.Tensor:
_pre = logits[:, :2, :, :, :]
_in = logits[:,
2:3, :, :, :] + logits[:,
3:4, :, :, :] + logits[:,
4:5, :, :, :]
3:4, :, :, :] + logits[:, 4:
5, :, :, :]
_post = logits[:, 5:, :, :, :]

return torch.cat([_pre, _in, _post], dim=1)
raise ValueError(
f"Unknown `ca_mode` ({ca_mode}). `ca_mode` must be 1/2/3, 1/23 or 123"
)
f"Unknown `ca_mode` ({ca_mode}). `ca_mode` must be 1/2/3, 1/23 or 123")


def predict(mris: list,
Expand Down
11 changes: 6 additions & 5 deletions tests/test_hsf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,8 @@ def config(models_path):
"gpu_mem_limit": 2147483648
}
], "CPUExecutionProvider"],
"batch_size": 1
"batch_size":
1
}
},
"roiloc": {
Expand Down Expand Up @@ -102,9 +103,8 @@ def config(models_path):
"models": {
"model.onnx": {
"url":
"https://zenodo.org/record/6457484/files/arunet_3.0.0_single.onnx?download=1",
"xxh3_64":
"71edec9011f7f304"
"https://zenodo.org/record/6457484/files/arunet_3.0.0_single.onnx?download=1",
"xxh3_64": "71edec9011f7f304"
}
},
"segmentation": {
Expand Down Expand Up @@ -220,7 +220,8 @@ def test_multispectrality(models_path):
}
})

mri = hsf.roiloc_wrapper.load_from_config(models_path, "sub0_tse.nii.gz")[0]
mri = hsf.roiloc_wrapper.load_from_config(models_path,
"sub0_tse.nii.gz")[0]
second_contrast = hsf.multispectrality.get_second_contrast(
mri, "sub0_tse.nii.gz")

Expand Down
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