Moved hivtb configs to data

src/ptbench/data/hivtb/fold_8_rgb.py

+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""HIV-TB dataset for TB detection (cross validation fold 8)
+
+* Split reference: none (stratified kfolding)
+* This configuration resolution: 512 x 512 (default)
+* See :py:mod:`ptbench.data.hivtb` for dataset details
+"""
+
+from clapper.logging import setup
+
+from .. import return_subsets
+from ..base_datamodule import BaseDataModule
+from . import _maker
+
+logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+
+
+class DefaultModule(BaseDataModule):
+    def __init__(
+        self,
+        train_batch_size=1,
+        predict_batch_size=1,
+        drop_incomplete_batch=False,
+        multiproc_kwargs=None,
+    ):
+        super().__init__(
+            train_batch_size=train_batch_size,
+            predict_batch_size=predict_batch_size,
+            drop_incomplete_batch=drop_incomplete_batch,
+            multiproc_kwargs=multiproc_kwargs,
+        )
+
+    def setup(self, stage: str):
+        self.dataset = _maker("fold_8", RGB=True)
+        (
+            self.train_dataset,
+            self.validation_dataset,
+            self.extra_validation_datasets,
+            self.predict_dataset,
+        ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule

src/ptbench/data/hivtb/fold_9.py

+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""HIV-TB dataset for TB detection (cross validation fold 9)
+
+* Split reference: none (stratified kfolding)
+* This configuration resolution: 512 x 512 (default)
+* See :py:mod:`ptbench.data.hivtb` for dataset details
+"""
+
+from clapper.logging import setup
+
+from .. import return_subsets
+from ..base_datamodule import BaseDataModule
+from . import _maker
+
+logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+
+
+class DefaultModule(BaseDataModule):
+    def __init__(
+        self,
+        train_batch_size=1,
+        predict_batch_size=1,
+        drop_incomplete_batch=False,
+        multiproc_kwargs=None,
+    ):
+        super().__init__(
+            train_batch_size=train_batch_size,
+            predict_batch_size=predict_batch_size,
+            drop_incomplete_batch=drop_incomplete_batch,
+            multiproc_kwargs=multiproc_kwargs,
+        )
+
+    def setup(self, stage: str):
+        self.dataset = _maker("fold_9")
+        (
+            self.train_dataset,
+            self.validation_dataset,
+            self.extra_validation_datasets,
+            self.predict_dataset,
+        ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule

src/ptbench/data/hivtb/fold_9_rgb.py

+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""HIV-TB dataset for TB detection (cross validation fold 9)
+
+* Split reference: none (stratified kfolding)
+* This configuration resolution: 512 x 512 (default)
+* See :py:mod:`ptbench.data.hivtb` for dataset details
+"""
+
+from clapper.logging import setup
+
+from .. import return_subsets
+from ..base_datamodule import BaseDataModule
+from . import _maker
+
+logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+
+
+class DefaultModule(BaseDataModule):
+    def __init__(
+        self,
+        train_batch_size=1,
+        predict_batch_size=1,
+        drop_incomplete_batch=False,
+        multiproc_kwargs=None,
+    ):
+        super().__init__(
+            train_batch_size=train_batch_size,
+            predict_batch_size=predict_batch_size,
+            drop_incomplete_batch=drop_incomplete_batch,
+            multiproc_kwargs=multiproc_kwargs,
+        )
+
+    def setup(self, stage: str):
+        self.dataset = _maker("fold_9", RGB=True)
+        (
+            self.train_dataset,
+            self.validation_dataset,
+            self.extra_validation_datasets,
+            self.predict_dataset,
+        ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule