diff --git a/pyproject.toml b/pyproject.toml
index 0a83ba62bb8938c36e967bc0dcc415552f7a6175..910ff540249164d41d13a00824dfcdfc5df94552 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -427,6 +427,9 @@ lwnet = "mednet.libs.segmentation.config.models.lwnet"
chasedb1 = "mednet.libs.segmentation.config.data.chasedb1.first_annotator"
chasedb1-2nd = "mednet.libs.segmentation.config.data.chasedb1.second_annotator"
+# cxr8 - cxr
+cxr8 = "mednet.libs.segmentation.config.data.cxr8.default"
+
# drive dataset - retinography
drive = "mednet.libs.segmentation.config.data.drive.default"
diff --git a/src/mednet/libs/classification/config/data/hivtb/datamodule.py b/src/mednet/libs/classification/config/data/hivtb/datamodule.py
index a93606bb6547b5cd196ce58100613a41ed130169..93627558cc62239c7341e412895c1d8931fb3910 100644
--- a/src/mednet/libs/classification/config/data/hivtb/datamodule.py
+++ b/src/mednet/libs/classification/config/data/hivtb/datamodule.py
@@ -15,10 +15,8 @@ from mednet.libs.classification.data.typing import (
)
from mednet.libs.common.data.datamodule import CachingDataModule
from mednet.libs.common.data.image_utils import remove_black_borders
-
from mednet.libs.common.data.split import make_split
from mednet.libs.common.data.typing import Sample
-
from torchvision.transforms.functional import to_tensor
from ....utils.rc import load_rc
@@ -127,7 +125,7 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/classification/config/data/indian/datamodule.py b/src/mednet/libs/classification/config/data/indian/datamodule.py
index 63ff00c284201f797dc4eb51f5b1e7ef8c723f4f..efcce8e7e7e8b3d50e94c34ebfe394ca4aff933b 100644
--- a/src/mednet/libs/classification/config/data/indian/datamodule.py
+++ b/src/mednet/libs/classification/config/data/indian/datamodule.py
@@ -11,9 +11,8 @@ import pathlib
from mednet.libs.common.data.datamodule import CachingDataModule
from mednet.libs.common.data.split import make_split
-from mednet.libs.classification.data.typing import (
- ClassificationRawDataLoader as _ClassificationRawDataLoader,
-)
+from ....config.data.shenzhen.datamodule import ClassificationRawDataLoader
+
CONFIGURATION_KEY_DATADIR = "datadir." + (__name__.rsplit(".", 2)[-2])
"""Key to search for in the configuration file for the root directory of this
database."""
@@ -64,8 +63,9 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(
+ config_variable=CONFIGURATION_KEY_DATADIR
+ ),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
-
diff --git a/src/mednet/libs/classification/config/data/montgomery/datamodule.py b/src/mednet/libs/classification/config/data/montgomery/datamodule.py
index 2afbb8d6cd478371bff6f2828a737a5d93959183..8ccd7536981665214855016d98c482ef6dd90acc 100644
--- a/src/mednet/libs/classification/config/data/montgomery/datamodule.py
+++ b/src/mednet/libs/classification/config/data/montgomery/datamodule.py
@@ -15,10 +15,8 @@ from mednet.libs.classification.data.typing import (
)
from mednet.libs.common.data.datamodule import CachingDataModule
from mednet.libs.common.data.image_utils import remove_black_borders
-
from mednet.libs.common.data.split import make_split
from mednet.libs.common.data.typing import Sample
-
from torchvision.transforms.functional import to_tensor
from ....utils.rc import load_rc
@@ -28,7 +26,7 @@ CONFIGURATION_KEY_DATADIR = "datadir." + (__name__.rsplit(".", 2)[-2])
database."""
-class RawDataLoader(_BaseRawDataLoader):
+class ClassificationRawDataLoader(_ClassificationRawDataLoader):
"""A specialized raw-data-loader for the Montgomery dataset.
Parameters
@@ -140,7 +138,7 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py b/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py
index 0edb1aef91c0f0a19acbd83bf6dfcc1103b89fae..b022d50a0e46edfdcf2572a18a736f558284d7ec 100644
--- a/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py
+++ b/src/mednet/libs/classification/config/data/nih_cxr14/datamodule.py
@@ -14,10 +14,8 @@ from mednet.libs.classification.data.typing import (
ClassificationRawDataLoader as _ClassificationRawDataLoader,
)
from mednet.libs.common.data.datamodule import CachingDataModule
-
from mednet.libs.common.data.split import make_split
from mednet.libs.common.data.typing import Sample
-
from torchvision.transforms.functional import to_tensor
from ....utils.rc import load_rc
@@ -86,7 +84,9 @@ class ClassificationRawDataLoader(_ClassificationRawDataLoader):
# for folder lookup efficiency, data is split into subfolders
# each original file is on the subfolder `f[:5]/f`, where f
# is the original file basename
- file_path = file_path.parent / file_path.name[:5] / file_path.name
+ file_path = pathlib.Path(
+ file_path.parent / file_path.name[:5] / file_path.name
+ )
# N.B.: some NIH CXR-14 images are encoded as color PNGs with an alpha
# channel. Most, are grayscale PNGs
@@ -176,7 +176,7 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/classification/config/data/padchest/datamodule.py b/src/mednet/libs/classification/config/data/padchest/datamodule.py
index 51a20bbdbc9392ce7917cb44b6db3a0233f9a857..0c6dfba30d92ee353010ea2220220c96c19fb8e4 100644
--- a/src/mednet/libs/classification/config/data/padchest/datamodule.py
+++ b/src/mednet/libs/classification/config/data/padchest/datamodule.py
@@ -16,10 +16,8 @@ from mednet.libs.classification.data.typing import (
)
from mednet.libs.common.data.datamodule import CachingDataModule
from mednet.libs.common.data.image_utils import remove_black_borders
-
from mednet.libs.common.data.split import make_split
from mednet.libs.common.data.typing import Sample
-
from torchvision.transforms.functional import to_tensor
from ....utils.rc import load_rc
@@ -329,7 +327,7 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/classification/config/data/shenzhen/datamodule.py b/src/mednet/libs/classification/config/data/shenzhen/datamodule.py
index 45aa327e06abbb22d9f7bbbc6f957a49291a4a06..b1b78c574b60f9b00fc48cd88d43746265cdc8b4 100644
--- a/src/mednet/libs/classification/config/data/shenzhen/datamodule.py
+++ b/src/mednet/libs/classification/config/data/shenzhen/datamodule.py
@@ -15,10 +15,8 @@ from mednet.libs.classification.data.typing import (
)
from mednet.libs.common.data.datamodule import CachingDataModule
from mednet.libs.common.data.image_utils import remove_black_borders
-
from mednet.libs.common.data.split import make_split
from mednet.libs.common.data.typing import Sample
-
from torchvision.transforms.functional import to_tensor
from ....utils.rc import load_rc
@@ -139,7 +137,7 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/classification/config/data/tbpoc/datamodule.py b/src/mednet/libs/classification/config/data/tbpoc/datamodule.py
index f92afc9d2e2ac141f7c4889db32e72c187f93f13..7c943b3e503c69bf1efb1f77be1468e2be910bf9 100644
--- a/src/mednet/libs/classification/config/data/tbpoc/datamodule.py
+++ b/src/mednet/libs/classification/config/data/tbpoc/datamodule.py
@@ -11,10 +11,8 @@ from mednet.libs.classification.data.typing import (
)
from mednet.libs.common.data.datamodule import CachingDataModule
from mednet.libs.common.data.image_utils import remove_black_borders
-
from mednet.libs.common.data.split import make_split
from mednet.libs.common.data.typing import Sample
-
from torchvision.transforms.functional import to_tensor
from ....utils.rc import load_rc
@@ -125,7 +123,7 @@ class DataModule(CachingDataModule):
assert __package__ is not None
super().__init__(
database_split=make_split(__package__, split_filename),
- raw_data_loader=_ClassificationRawDataLoader(),
- database_name=__package__.rsplit(".", 1)[1],
+ raw_data_loader=ClassificationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/classification/tests/test_hivtb.py b/src/mednet/libs/classification/tests/test_hivtb.py
index 2075f91a4f184d1ae1f191123144fc25c155c102..91258839bb00566b1330dd364a5f3555e4e4c47f 100644
--- a/src/mednet/libs/classification/tests/test_hivtb.py
+++ b/src/mednet/libs/classification/tests/test_hivtb.py
@@ -39,7 +39,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.hivtb", f"{split}.json"),
+ make_split("mednet.libs.classification.config.data.hivtb", f"{split}.json"),
lengths=lengths,
prefixes=("HIV-TB_Algorithm_study_X-rays",),
possible_labels=(0, 1),
diff --git a/src/mednet/libs/classification/tests/test_indian.py b/src/mednet/libs/classification/tests/test_indian.py
index c55f82b388942d2cea721de5cea428797bc8aa06..952bc9b76727e72e25c41efe3bdfeb1de8e17cc8 100644
--- a/src/mednet/libs/classification/tests/test_indian.py
+++ b/src/mednet/libs/classification/tests/test_indian.py
@@ -43,7 +43,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.indian", f"{split}.json"),
+ make_split("mednet.libs.classification.config.data.indian", f"{split}.json"),
lengths=lengths,
prefixes=("DatasetA/Training", "DatasetA/Testing"),
possible_labels=(0, 1),
diff --git a/src/mednet/libs/classification/tests/test_montgomery.py b/src/mednet/libs/classification/tests/test_montgomery.py
index 24ff0c9eae169475069867aac7051e94e8981b73..ef8715ea8b8caf2e3047831bcccd01863e53e25e 100644
--- a/src/mednet/libs/classification/tests/test_montgomery.py
+++ b/src/mednet/libs/classification/tests/test_montgomery.py
@@ -41,7 +41,9 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.montgomery", f"{split}.json"),
+ make_split(
+ "mednet.libs.classification.config.data.montgomery", f"{split}.json"
+ ),
lengths=lengths,
prefixes=("CXR_png/MCUCXR_0",),
possible_labels=(0, 1),
@@ -114,12 +116,8 @@ def test_loading(database_checkers, name: str, dataset: str):
@pytest.mark.skip_if_rc_var_not_set("datadir.montgomery")
def test_raw_transforms_image_quality(database_checkers, datadir: pathlib.Path):
datamodule = importlib.import_module(
-<<<<<<< HEAD
- ".default", "mednet.config.data.montgomery"
-=======
".default",
"mednet.libs.classification.config.data.montgomery",
->>>>>>> b1ea1c0 ([mednet] Start reorganizing into monorepo)
).datamodule
datamodule.model_transforms = []
diff --git a/src/mednet/libs/classification/tests/test_nih_cxr14.py b/src/mednet/libs/classification/tests/test_nih_cxr14.py
index e3466a8f53d055743c9888c92800cb9375decca8..da5504ba37695368c7a77a3bc1e25b5a51a14b89 100644
--- a/src/mednet/libs/classification/tests/test_nih_cxr14.py
+++ b/src/mednet/libs/classification/tests/test_nih_cxr14.py
@@ -31,7 +31,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.nih_cxr14", f"{split}"),
+ make_split("mednet.libs.classification.config.data.nih_cxr14", f"{split}"),
lengths=lengths,
prefixes=("images/000",),
possible_labels=(0, 1),
diff --git a/src/mednet/libs/classification/tests/test_padchest.py b/src/mednet/libs/classification/tests/test_padchest.py
index 8e420e17ea9cadd26f3886f3c4af09c4a533ea1e..af6007df4abb53f4d14fd964da4ad9e129eca804 100644
--- a/src/mednet/libs/classification/tests/test_padchest.py
+++ b/src/mednet/libs/classification/tests/test_padchest.py
@@ -36,7 +36,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.padchest", split),
+ make_split("mednet.libs.classification.config.data.padchest", split),
lengths=lengths,
prefixes=("",),
possible_labels=(0, 1),
diff --git a/src/mednet/libs/classification/tests/test_shenzhen.py b/src/mednet/libs/classification/tests/test_shenzhen.py
index ec40545ad811a4281311179f553f92bbd1746ae3..afc55032dc27fa6e29d6515387f37888d2ed8f20 100644
--- a/src/mednet/libs/classification/tests/test_shenzhen.py
+++ b/src/mednet/libs/classification/tests/test_shenzhen.py
@@ -40,7 +40,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.shenzhen", f"{split}.json"),
+ make_split("mednet.libs.classification.config.data.shenzhen", f"{split}.json"),
lengths=lengths,
prefixes=("CXR_png/CHNCXR_0",),
possible_labels=(0, 1),
diff --git a/src/mednet/libs/classification/tests/test_tbpoc.py b/src/mednet/libs/classification/tests/test_tbpoc.py
index 3fa562406403c0e9dd6fa6ba4974e64195cafa90..81288179e3cc7590726483faefa300344c50edeb 100644
--- a/src/mednet/libs/classification/tests/test_tbpoc.py
+++ b/src/mednet/libs/classification/tests/test_tbpoc.py
@@ -39,7 +39,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.tbpoc", f"{split}.json"),
+ make_split("mednet.libs.classification.config.data.tbpoc", f"{split}.json"),
lengths=lengths,
prefixes=(
"TBPOC_CXR/TBPOC-",
diff --git a/src/mednet/libs/classification/tests/test_tbx11k.py b/src/mednet/libs/classification/tests/test_tbx11k.py
index be965f9a906eb2ff99a6a3ff8574c8de19d9ac31..932b87825a6af716e6a986bbbe0a2f18f2a2bdd5 100644
--- a/src/mednet/libs/classification/tests/test_tbx11k.py
+++ b/src/mednet/libs/classification/tests/test_tbx11k.py
@@ -142,7 +142,7 @@ def test_protocol_consistency(
from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split("mednet.config.data.tbx11k", f"{split}.json"),
+ make_split("mednet.libs.classification.config.data.tbx11k", f"{split}.json"),
lengths=lengths,
prefixes=prefixes,
possible_labels=(0, 1),
diff --git a/src/mednet/libs/common/models/model.py b/src/mednet/libs/common/models/model.py
index d01cc14a4c7e58d10cb8adb5fe875c9198761d9e..53a14658fbdf6edadd8190c9ae2f0e308e7cd54b 100644
--- a/src/mednet/libs/common/models/model.py
+++ b/src/mednet/libs/common/models/model.py
@@ -11,8 +11,8 @@ import torch.nn
import torch.optim.optimizer
import torch.utils.data
import torchvision.transforms
+from mednet.libs.common.data.typing import TransformSequence
-from medbase.data.typing import TransformSequence
from .loss_weights import get_positive_weights
from .typing import Checkpoint
@@ -224,7 +224,5 @@ class Model(pl.LightningModule):
logger.warning(
"Datamodule does not contain a validation dataloader. The training dataloader will be used instead."
)
- validation_weights = get_positive_weights(
- datamodule.train_dataloader()
- )
+ validation_weights = get_positive_weights(datamodule.train_dataloader())
self._validation_loss_arguments["pos_weight"] = validation_weights
diff --git a/src/mednet/libs/common/tests/test_database_split.py b/src/mednet/libs/common/tests/test_database_split.py
index e77c4cbf8c932f39f5a794b10a23778a1e48d837..114dd1bac72185685e41544bf3b7f393660c18f7 100644
--- a/src/mednet/libs/common/tests/test_database_split.py
+++ b/src/mednet/libs/common/tests/test_database_split.py
@@ -3,31 +3,9 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""Test code for datasets."""
-<<<<<<< HEAD
-from medbase.data.split import JSONDatabaseSplit
-=======
-from mednet.libs.common.data.split import CSVDatabaseSplit, JSONDatabaseSplit
+from mednet.libs.common.data.split import JSONDatabaseSplit
-def test_csv_loading(datadir):
- # tests if we can build a simple CSV loader for the Iris Flower dataset
- database_split = CSVDatabaseSplit(datadir)
-
- assert len(database_split["iris-train"]) == 75
- for k in database_split["iris-train"]:
- for f in range(4):
- assert isinstance(k[f], str) # csv only loads stringd
- assert isinstance(k[4], str)
-
- assert len(database_split["iris-test"]) == 75
- for k in database_split["iris-test"]:
- for f in range(4):
- assert isinstance(k[f], str) # csv only loads stringd
- assert isinstance(k[4], str)
- assert k[4] in ("Iris-setosa", "Iris-versicolor", "Iris-virginica")
-
->>>>>>> b1ea1c0 ([mednet] Start reorganizing into monorepo)
-
def test_json_loading(datadir):
# tests if we can build a simple JSON loader for the Iris Flower dataset
diff --git a/src/mednet/libs/segmentation/config/data/chasedb1/datamodule.py b/src/mednet/libs/segmentation/config/data/chasedb1/datamodule.py
index f8dfa0bc895d49e77f4191259b7ef4d67f8a2e8b..e635ea2903b5a05c174c2b4686935bd3b9f8178f 100644
--- a/src/mednet/libs/segmentation/config/data/chasedb1/datamodule.py
+++ b/src/mednet/libs/segmentation/config/data/chasedb1/datamodule.py
@@ -3,15 +3,14 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""CHASE-DB1 dataset for Vessel Segmentation."""
-import importlib.resources
import os
-from pathlib import Path
+import pathlib
import PIL.Image
import pkg_resources
from mednet.libs.common.data.datamodule import CachingDataModule
-from mednet.libs.common.data.split import JSONDatabaseSplit
-from mednet.libs.common.data.typing import DatabaseSplit, Sample
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
from mednet.libs.segmentation.data.typing import (
SegmentationRawDataLoader as _SegmentationRawDataLoader,
)
@@ -28,16 +27,18 @@ database."""
class SegmentationRawDataLoader(_SegmentationRawDataLoader):
"""A specialized raw-data-loader for the Chase-db1 dataset."""
- datadir: str
+ datadir: pathlib.Path
"""This variable contains the base directory where the database raw data is
stored."""
def __init__(self):
- self.datadir = load_rc().get(
- CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir)
+ self.datadir = pathlib.Path(
+ load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
)
- self._pkg_path = pkg_resources.resource_filename(__name__, "masks")
+ self._pkg_path = pathlib.Path(
+ pkg_resources.resource_filename(__name__, "masks")
+ )
def sample(self, sample: tuple[str, str, str]) -> Sample:
"""Load a single image sample from the disk.
@@ -53,20 +54,16 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
The sample representation.
"""
- image = PIL.Image.open(Path(self.datadir) / str(sample[0])).convert(
- mode="RGB"
- )
+ image = PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB")
tensor = tv_tensors.Image(to_tensor(image))
target = tv_tensors.Image(
to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[1])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self.datadir / sample[1]).convert(mode="1", dither=None)
)
)
mask = tv_tensors.Mask(
to_tensor(
- PIL.Image.open(Path(self._pkg_path) / str(sample[2])).convert(
+ PIL.Image.open(self._pkg_path / sample[2]).convert(
mode="1", dither=None
)
)
@@ -75,24 +72,6 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
-def make_split(basename: str) -> DatabaseSplit:
- """Return a database split for the Chase-db1 database.
-
- Parameters
- ----------
- basename
- Name of the .json file containing the split to load.
-
- Returns
- -------
- An instance of DatabaseSplit.
- """
-
- return JSONDatabaseSplit(
- importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
- )
-
-
class DataModule(CachingDataModule):
"""CHASE-DB1 dataset for Vessel Segmentation.
@@ -129,7 +108,10 @@ class DataModule(CachingDataModule):
"""
def __init__(self, split_filename: str):
+ assert __package__ is not None
super().__init__(
- database_split=make_split(split_filename),
+ database_split=make_split(__package__, split_filename),
raw_data_loader=SegmentationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
+ split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/segmentation/config/data/drive/datamodule.py b/src/mednet/libs/segmentation/config/data/drive/datamodule.py
index b3a2a76730d5be54881d57dccf08e941dceff260..14aa36aeab721b7213adc5a4a552877d2ae4a0a7 100644
--- a/src/mednet/libs/segmentation/config/data/drive/datamodule.py
+++ b/src/mednet/libs/segmentation/config/data/drive/datamodule.py
@@ -3,14 +3,13 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""COVD-DRIVE for Vessel Segmentation."""
-import importlib.resources
import os
-from pathlib import Path
+import pathlib
import PIL.Image
from mednet.libs.common.data.datamodule import CachingDataModule
-from mednet.libs.common.data.split import JSONDatabaseSplit
-from mednet.libs.common.data.typing import DatabaseSplit, Sample
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
from mednet.libs.common.models.transforms import crop_image_to_mask
from mednet.libs.segmentation.data.typing import (
SegmentationRawDataLoader as _SegmentationRawDataLoader,
@@ -28,13 +27,13 @@ database."""
class SegmentationRawDataLoader(_SegmentationRawDataLoader):
"""A specialized raw-data-loader for the Drive dataset."""
- datadir: str
+ datadir: pathlib.Path
"""This variable contains the base directory where the database raw data is
stored."""
def __init__(self):
- self.datadir = load_rc().get(
- CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir)
+ self.datadir = pathlib.Path(
+ load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
)
def sample(self, sample: tuple[str, str, str]) -> Sample:
@@ -51,20 +50,12 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
The sample representation.
"""
- image = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[0])).convert(
- mode="RGB"
- )
- )
+ image = to_tensor(PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB"))
target = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[1])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self.datadir / sample[1]).convert(mode="1", dither=None)
)
mask = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[2])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self.datadir / sample[2]).convert(mode="1", dither=None)
)
tensor = tv_tensors.Image(crop_image_to_mask(image, mask))
@@ -74,24 +65,6 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
-def make_split(basename: str) -> DatabaseSplit:
- """Return a database split for the Drive database.
-
- Parameters
- ----------
- basename
- Name of the .json file containing the split to load.
-
- Returns
- -------
- An instance of DatabaseSplit.
- """
-
- return JSONDatabaseSplit(
- importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
- )
-
-
class DataModule(CachingDataModule):
"""DRIVE dataset for Vessel Segmentation.
@@ -117,7 +90,10 @@ class DataModule(CachingDataModule):
"""
def __init__(self, split_filename: str):
+ assert __package__ is not None
super().__init__(
- database_split=make_split(split_filename),
+ database_split=make_split(__package__, split_filename),
raw_data_loader=SegmentationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
+ split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/segmentation/config/data/iostar/datamodule.py b/src/mednet/libs/segmentation/config/data/iostar/datamodule.py
index 72500523a933a0bd60e50d9f72ea296281799ca4..50df56780bdf891c26e47195e4e073d0ccb45fca 100644
--- a/src/mednet/libs/segmentation/config/data/iostar/datamodule.py
+++ b/src/mednet/libs/segmentation/config/data/iostar/datamodule.py
@@ -3,14 +3,13 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""IOSTAR (training set) for Vessel and Optic-Disc Segmentation."""
-import importlib.resources
import os
-from pathlib import Path
+import pathlib
import PIL.Image
from mednet.libs.common.data.datamodule import CachingDataModule
-from mednet.libs.common.data.split import JSONDatabaseSplit
-from mednet.libs.common.data.typing import DatabaseSplit, Sample
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
from mednet.libs.common.models.transforms import crop_image_to_mask
from mednet.libs.segmentation.data.typing import (
SegmentationRawDataLoader as _SegmentationRawDataLoader,
@@ -28,13 +27,13 @@ database."""
class SegmentationRawDataLoader(_SegmentationRawDataLoader):
"""A specialized raw-data-loader for the iostar dataset."""
- datadir: str
+ datadir: pathlib.Path
"""This variable contains the base directory where the database raw data is
stored."""
def __init__(self):
- self.datadir = load_rc().get(
- CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir)
+ self.datadir = pathlib.Path(
+ load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
)
def sample(self, sample: tuple[str, str, str]) -> Sample:
@@ -52,22 +51,14 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
The sample representation.
"""
- image = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[0])).convert(
- mode="RGB"
- )
- )
+ image = to_tensor(PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB"))
target = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[1])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self.datadir / sample[1]).convert(mode="1", dither=None)
)
mask = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[2])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self.datadir / sample[2]).convert(mode="1", dither=None)
)
tensor = tv_tensors.Image(crop_image_to_mask(image, mask))
@@ -77,24 +68,6 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
-def make_split(basename: str) -> DatabaseSplit:
- """Return a database split for the iostar database.
-
- Parameters
- ----------
- basename
- Name of the .json file containing the split to load.
-
- Returns
- -------
- An instance of DatabaseSplit.
- """
-
- return JSONDatabaseSplit(
- importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
- )
-
-
class DataModule(CachingDataModule):
"""IOSTAR (training set) for Vessel and Optic-Disc Segmentation.
@@ -123,7 +96,10 @@ class DataModule(CachingDataModule):
"""
def __init__(self, split_filename: str):
+ assert __package__ is not None
super().__init__(
- database_split=make_split(split_filename),
+ database_split=make_split(__package__, split_filename),
raw_data_loader=SegmentationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
+ split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/segmentation/config/data/montgomery/datamodule.py b/src/mednet/libs/segmentation/config/data/montgomery/datamodule.py
index 3828022c58caf09b36ef2eccf097818ecf6f5e50..f24b9925a52ba7f38ffc0ce0e28180f9f9c9683c 100644
--- a/src/mednet/libs/segmentation/config/data/montgomery/datamodule.py
+++ b/src/mednet/libs/segmentation/config/data/montgomery/datamodule.py
@@ -3,17 +3,16 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""Montgomery DataModule for TB detection."""
-import importlib.resources
import os
-from pathlib import Path
+import pathlib
import numpy as np
import PIL.Image
import pkg_resources
import torch
from mednet.libs.common.data.datamodule import CachingDataModule
-from mednet.libs.common.data.split import JSONDatabaseSplit
-from mednet.libs.common.data.typing import DatabaseSplit, Sample
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
from mednet.libs.segmentation.data.typing import (
SegmentationRawDataLoader as _SegmentationRawDataLoader,
)
@@ -30,15 +29,17 @@ database."""
class SegmentationRawDataLoader(_SegmentationRawDataLoader):
"""A specialized raw-data-loader for the Montgomery dataset."""
- datadir: str
+ datadir: pathlib.Path
"""This variable contains the base directory where the database raw data is
stored."""
def __init__(self):
- self.datadir = load_rc().get(
- CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir)
+ self.datadir = pathlib.Path(
+ load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
+ )
+ self._pkg_path = pathlib.Path(
+ pkg_resources.resource_filename(__name__, "masks")
)
- self._pkg_path = pkg_resources.resource_filename(__name__, "masks")
def sample(self, sample: tuple[str, str, str]) -> Sample:
"""Load a single image sample from the disk.
@@ -55,9 +56,7 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
The sample representation.
"""
- image = PIL.Image.open(Path(self.datadir) / str(sample[0])).convert(
- mode="RGB"
- )
+ image = PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB")
tensor = tv_tensors.Image(to_tensor(image))
# Combine left and right lung masks into a single tensor
@@ -65,14 +64,14 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
to_tensor(
np.ma.mask_or(
np.asarray(
- PIL.Image.open(
- Path(self.datadir) / str(sample[1])
- ).convert(mode="1", dither=None)
+ PIL.Image.open(self.datadir / sample[1]).convert(
+ mode="1", dither=None
+ )
),
np.asarray(
- PIL.Image.open(
- Path(self.datadir) / str(sample[2])
- ).convert(mode="1", dither=None)
+ PIL.Image.open(self.datadir / sample[2]).convert(
+ mode="1", dither=None
+ )
),
)
).float()
@@ -83,24 +82,6 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
-def make_split(basename: str) -> DatabaseSplit:
- """Return a database split for the Montgomery database.
-
- Parameters
- ----------
- basename
- Name of the .json file containing the split to load.
-
- Returns
- -------
- An instance of DatabaseSplit.
- """
-
- return JSONDatabaseSplit(
- importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
- )
-
-
class DataModule(CachingDataModule):
"""Montgomery DataModule for TB detection.
@@ -140,7 +121,10 @@ class DataModule(CachingDataModule):
"""
def __init__(self, split_filename: str):
+ assert __package__ is not None
super().__init__(
- database_split=make_split(split_filename),
+ database_split=make_split(__package__, split_filename),
raw_data_loader=SegmentationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
+ split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/segmentation/config/data/stare/datamodule.py b/src/mednet/libs/segmentation/config/data/stare/datamodule.py
index d037a6947169d7c3659f17f5064fe921f843f5ef..9bf18d18827707c1d8ad868c815028802326970a 100644
--- a/src/mednet/libs/segmentation/config/data/stare/datamodule.py
+++ b/src/mednet/libs/segmentation/config/data/stare/datamodule.py
@@ -3,15 +3,14 @@
# SPDX-License-Identifier: GPL-3.0-or-later
"""STARE dataset for Vessel Segmentation."""
-import importlib.resources
import os
-from pathlib import Path
+import pathlib
import PIL.Image
import pkg_resources
from mednet.libs.common.data.datamodule import CachingDataModule
-from mednet.libs.common.data.split import JSONDatabaseSplit
-from mednet.libs.common.data.typing import DatabaseSplit, Sample
+from mednet.libs.common.data.split import make_split
+from mednet.libs.common.data.typing import Sample
from mednet.libs.common.models.transforms import crop_image_to_mask
from mednet.libs.segmentation.data.typing import (
SegmentationRawDataLoader as _SegmentationRawDataLoader,
@@ -29,15 +28,17 @@ database."""
class SegmentationRawDataLoader(_SegmentationRawDataLoader):
"""A specialized raw-data-loader for the Stare dataset."""
- datadir: str
+ datadir: pathlib.Path
"""This variable contains the base directory where the database raw data is
stored."""
def __init__(self):
- self.datadir = load_rc().get(
- CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir)
+ self.datadir = pathlib.Path(
+ load_rc().get(CONFIGURATION_KEY_DATADIR, os.path.realpath(os.curdir))
+ )
+ self._pkg_path = pathlib.Path(
+ pkg_resources.resource_filename(__name__, "masks")
)
- self._pkg_path = pkg_resources.resource_filename(__name__, "masks")
def sample(self, sample: tuple[str, str, str]) -> Sample:
"""Load a single image sample from the disk.
@@ -54,22 +55,14 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
The sample representation.
"""
- image = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[0])).convert(
- mode="RGB"
- )
- )
+ image = to_tensor(PIL.Image.open(self.datadir / sample[0]).convert(mode="RGB"))
target = to_tensor(
- PIL.Image.open(Path(self.datadir) / str(sample[1])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self.datadir / sample[1]).convert(mode="1", dither=None)
)
mask = to_tensor(
- PIL.Image.open(Path(self._pkg_path) / str(sample[2])).convert(
- mode="1", dither=None
- )
+ PIL.Image.open(self._pkg_path / sample[2]).convert(mode="1", dither=None)
)
tensor = tv_tensors.Image(crop_image_to_mask(image, mask))
@@ -79,24 +72,6 @@ class SegmentationRawDataLoader(_SegmentationRawDataLoader):
return tensor, dict(target=target, mask=mask, name=sample[0]) # type: ignore[arg-type]
-def make_split(basename: str) -> DatabaseSplit:
- """Return a database split for the Stare database.
-
- Parameters
- ----------
- basename
- Name of the .json file containing the split to load.
-
- Returns
- -------
- An instance of DatabaseSplit.
- """
-
- return JSONDatabaseSplit(
- importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
- )
-
-
class DataModule(CachingDataModule):
"""STARE dataset for Vessel Segmentation.
@@ -126,7 +101,10 @@ class DataModule(CachingDataModule):
"""
def __init__(self, split_filename: str):
+ assert __package__ is not None
super().__init__(
- database_split=make_split(split_filename),
+ database_split=make_split(__package__, split_filename),
raw_data_loader=SegmentationRawDataLoader(),
+ database_name=__package__.rsplit(".", 1)[1],
+ split_name=pathlib.Path(split_filename).stem,
)
diff --git a/src/mednet/libs/segmentation/tests/test_chasedb1.py b/src/mednet/libs/segmentation/tests/test_chasedb1.py
index 145bb4dbafeb5aea50e67211f6f2a3a7607c3279..77c8f2d60475939df42f243220deb6d728bb714a 100644
--- a/src/mednet/libs/segmentation/tests/test_chasedb1.py
+++ b/src/mednet/libs/segmentation/tests/test_chasedb1.py
@@ -28,12 +28,10 @@ def test_protocol_consistency(
split: str,
lengths: dict[str, int],
):
- from mednet.libs.segmentation.config.data.chasedb1.datamodule import (
- make_split,
- )
+ from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split(f"{split}.json"),
+ make_split("mednet.libs.segmentation.config.data.chasedb1", f"{split}.json"),
lengths=lengths,
)
@@ -91,8 +89,7 @@ def test_loading(database_checkers, name: str, dataset: str):
@pytest.mark.skip_if_rc_var_not_set("datadir.chasedb1")
def test_raw_transforms_image_quality(database_checkers, datadir):
reference_histogram_file = str(
- datadir
- / "histograms/raw_data/histograms_chasedb1_first_annotator.json",
+ datadir / "histograms/raw_data/histograms_chasedb1_first_annotator.json",
)
datamodule = importlib.import_module(
diff --git a/src/mednet/libs/segmentation/tests/test_cli_segmentation.py b/src/mednet/libs/segmentation/tests/test_cli_segmentation.py
index 412509c8d98d5938f0f359732c69ddaea305a1d3..7a824c04f00049b6f2f4c4af176db947a94910fc 100644
--- a/src/mednet/libs/segmentation/tests/test_cli_segmentation.py
+++ b/src/mednet/libs/segmentation/tests/test_cli_segmentation.py
@@ -84,10 +84,7 @@ def test_config_describe_drive():
runner = CliRunner()
result = runner.invoke(describe, ["drive"])
_assert_exit_0(result)
- assert (
- "DRIVE dataset for Vessel Segmentation (default protocol)."
- in result.output
- )
+ assert "DRIVE dataset for Vessel Segmentation (default protocol)." in result.output
def test_database_help():
@@ -154,6 +151,7 @@ def test_evaluate_help():
_check_help(evaluate)
+@pytest.mark.slow
@pytest.mark.skip_if_rc_var_not_set("datadir.drive")
def test_train_lwnet_drive(temporary_basedir):
from mednet.libs.common.utils.checkpointer import (
@@ -185,10 +183,7 @@ def test_train_lwnet_drive(temporary_basedir):
best = _get_checkpoint_from_alias(output_folder, "best")
assert best.name.endswith("epoch=0" + CHECKPOINT_EXTENSION)
- assert (
- len(list((output_folder / "logs").glob("events.out.tfevents.*")))
- == 1
- )
+ assert len(list((output_folder / "logs").glob("events.out.tfevents.*"))) == 1
assert (output_folder / "meta.json").exists()
keywords = {
@@ -209,6 +204,7 @@ def test_train_lwnet_drive(temporary_basedir):
)
+@pytest.mark.slow
@pytest.mark.skip_if_rc_var_not_set("datadir.drive")
def test_train_lwnet_drive_from_checkpoint(temporary_basedir):
from mednet.libs.common.utils.checkpointer import (
@@ -240,9 +236,7 @@ def test_train_lwnet_drive_from_checkpoint(temporary_basedir):
assert best.name.endswith("epoch=0" + CHECKPOINT_EXTENSION)
assert (output_folder / "meta.json").exists()
- assert (
- len(list((output_folder / "logs").glob("events.out.tfevents.*"))) == 1
- )
+ assert len(list((output_folder / "logs").glob("events.out.tfevents.*"))) == 1
with stdout_logging() as buf:
result = runner.invoke(
@@ -264,10 +258,7 @@ def test_train_lwnet_drive_from_checkpoint(temporary_basedir):
best = _get_checkpoint_from_alias(output_folder, "best")
assert (output_folder / "meta.json").exists()
- assert (
- len(list((output_folder / "logs").glob("events.out.tfevents.*")))
- == 2
- )
+ assert len(list((output_folder / "logs").glob("events.out.tfevents.*"))) == 2
keywords = {
r"^Loading dataset:`train` without caching. Trade-off: CPU RAM usage: less | Disk I/O: more.$": 1,
@@ -288,6 +279,7 @@ def test_train_lwnet_drive_from_checkpoint(temporary_basedir):
)
+@pytest.mark.slow
@pytest.mark.skip_if_rc_var_not_set("datadir.drive")
def test_predict_lwnet_drive(temporary_basedir, datadir):
from mednet.libs.common.utils.checkpointer import (
@@ -340,6 +332,7 @@ def test_predict_lwnet_drive(temporary_basedir, datadir):
)
+@pytest.mark.slow
@pytest.mark.skip_if_rc_var_not_set("datadir.drive")
def test_evaluate_lwnet_drive(temporary_basedir):
from mednet.libs.segmentation.scripts.evaluate import evaluate
@@ -388,6 +381,7 @@ def test_evaluate_lwnet_drive(temporary_basedir):
)
+@pytest.mark.slow
@pytest.mark.skip_if_rc_var_not_set("datadir.drive")
def test_experiment(temporary_basedir):
from mednet.libs.segmentation.scripts.experiment import experiment
@@ -409,9 +403,7 @@ def test_experiment(temporary_basedir):
_assert_exit_0(result)
assert (output_folder / "model" / "meta.json").exists()
- assert (
- output_folder / "model" / f"model-at-epoch={num_epochs-1}.ckpt"
- ).exists()
+ assert (output_folder / "model" / f"model-at-epoch={num_epochs-1}.ckpt").exists()
assert (output_folder / "predictions" / "predictions.json").exists()
assert (output_folder / "predictions" / "predictions.meta.json").exists()
diff --git a/src/mednet/libs/segmentation/tests/test_drive.py b/src/mednet/libs/segmentation/tests/test_drive.py
index d1a1301d47522459397e5b7b1b22b733a42faeb2..060d113238af73a47c45e12e6dc6cc2d65a671fd 100644
--- a/src/mednet/libs/segmentation/tests/test_drive.py
+++ b/src/mednet/libs/segmentation/tests/test_drive.py
@@ -27,12 +27,10 @@ def test_protocol_consistency(
split: str,
lengths: dict[str, int],
):
- from mednet.libs.segmentation.config.data.drive.datamodule import (
- make_split,
- )
+ from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split(f"{split}.json"),
+ make_split("mednet.libs.segmentation.config.data.drive", f"{split}.json"),
lengths=lengths,
)
@@ -110,8 +108,7 @@ def test_raw_transforms_image_quality(database_checkers, datadir):
)
def test_model_transforms_image_quality(database_checkers, datadir, model_name):
reference_histogram_file = str(
- datadir
- / f"histograms/models/histograms_{model_name}_drive_default.json",
+ datadir / f"histograms/models/histograms_{model_name}_drive_default.json",
)
datamodule = importlib.import_module(
diff --git a/src/mednet/libs/segmentation/tests/test_iostar.py b/src/mednet/libs/segmentation/tests/test_iostar.py
index b00439a38d54e49c0dddf8de380d50f779c6bf4a..e50967e0a6a74622f463f448a23b5365e05e8564 100644
--- a/src/mednet/libs/segmentation/tests/test_iostar.py
+++ b/src/mednet/libs/segmentation/tests/test_iostar.py
@@ -28,12 +28,10 @@ def test_protocol_consistency(
split: str,
lengths: dict[str, int],
):
- from mednet.libs.segmentation.config.data.iostar.datamodule import (
- make_split,
- )
+ from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split(f"{split}.json"),
+ make_split("mednet.libs.segmentation.config.data.iostar", f"{split}.json"),
lengths=lengths,
)
@@ -109,8 +107,7 @@ def test_raw_transforms_image_quality(database_checkers, datadir):
)
def test_model_transforms_image_quality(database_checkers, datadir, model_name):
reference_histogram_file = str(
- datadir
- / f"histograms/models/histograms_{model_name}_iostar_vessel.json",
+ datadir / f"histograms/models/histograms_{model_name}_iostar_vessel.json",
)
datamodule = importlib.import_module(
diff --git a/src/mednet/libs/segmentation/tests/test_montgomery.py b/src/mednet/libs/segmentation/tests/test_montgomery.py
index 16ab9bb7ae73957eaaafb9ccedca603469b16fc1..2f90200f70722edc54e4eda27e0dcc6ba5d870c8 100644
--- a/src/mednet/libs/segmentation/tests/test_montgomery.py
+++ b/src/mednet/libs/segmentation/tests/test_montgomery.py
@@ -27,12 +27,10 @@ def test_protocol_consistency(
split: str,
lengths: dict[str, int],
):
- from mednet.libs.segmentation.config.data.montgomery.datamodule import (
- make_split,
- )
+ from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split(f"{split}.json"),
+ make_split("mednet.libs.segmentation.config.data.montgomery", f"{split}.json"),
lengths=lengths,
)
@@ -112,8 +110,7 @@ def test_raw_transforms_image_quality(database_checkers, datadir):
)
def test_model_transforms_image_quality(database_checkers, datadir, model_name):
reference_histogram_file = str(
- datadir
- / f"histograms/models/histograms_{model_name}_montgomery_default.json",
+ datadir / f"histograms/models/histograms_{model_name}_montgomery_default.json",
)
datamodule = importlib.import_module(
diff --git a/src/mednet/libs/segmentation/tests/test_stare.py b/src/mednet/libs/segmentation/tests/test_stare.py
index 9b0038945da8c6648664dcd124add57eed898cf5..6268ec9f32cf53c4ba8377cd1f9bba77f011d12f 100644
--- a/src/mednet/libs/segmentation/tests/test_stare.py
+++ b/src/mednet/libs/segmentation/tests/test_stare.py
@@ -28,12 +28,10 @@ def test_protocol_consistency(
split: str,
lengths: dict[str, int],
):
- from mednet.libs.segmentation.config.data.stare.datamodule import (
- make_split,
- )
+ from mednet.libs.common.data.split import make_split
database_checkers.check_split(
- make_split(f"{split}.json"),
+ make_split("mednet.libs.segmentation.config.data.stare", f"{split}.json"),
lengths=lengths,
)