Moved nih_cxr14_re_pc configs to data

src/ptbench/configs/datasets/nih_cxr14_re_pc/__init__.py

-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-from torch.utils.data.dataset import ConcatDataset
-
-
-def _maker(protocol):
-    if protocol == "idiap":
-        from ..nih_cxr14_re import default as nih_cxr14_re
-        from ..padchest import no_tb_idiap as padchest_no_tb
-    else:
-        raise RuntimeError(f"Unsupported protocol: {protocol}")
-
-    nih_cxr14_re = nih_cxr14_re.dataset
-    padchest_no_tb = padchest_no_tb.dataset
-
-    dataset = {}
-    dataset["__train__"] = ConcatDataset(
-        [nih_cxr14_re["__train__"], padchest_no_tb["__train__"]]
-    )
-    dataset["train"] = ConcatDataset(
-        [nih_cxr14_re["train"], padchest_no_tb["train"]]
-    )
-    dataset["__valid__"] = ConcatDataset(
-        [nih_cxr14_re["__valid__"], padchest_no_tb["__valid__"]]
-    )
-    dataset["validation"] = ConcatDataset(
-        [nih_cxr14_re["validation"], padchest_no_tb["validation"]]
-    )
-    dataset["test"] = nih_cxr14_re["test"]
-
-    return dataset

src/ptbench/configs/datasets/nih_cxr14_re_pc/idiap.py → src/ptbench/data/nih_cxr14_re_pc/__init__.py

 # SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of NIH CXR14 relabeld and PadChest (normalized)
-datasets."""
-
-from . import _maker
-
-dataset = _maker("idiap")

src/ptbench/data/nih_cxr14_re_pc/idiap.py

+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""Aggregated dataset composed of NIH CXR14 relabeld and PadChest (normalized)
+datasets."""
+
+from clapper.logging import setup
+from torch.utils.data.dataset import ConcatDataset
+
+from .. import return_subsets
+from ..base_datamodule import BaseDataModule, get_dataset_from_module
+from ..nih_cxr14_re.default import datamodule as nih_cxr14_re_datamodule
+from ..padchest.no_tb_idiap import datamodule as padchest_no_tb_datamodule
+
+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,
+    ):
+        self.train_batch_size = train_batch_size
+        self.predict_batch_size = predict_batch_size
+        self.drop_incomplete_batch = drop_incomplete_batch
+        self.multiproc_kwargs = multiproc_kwargs
+
+        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):
+        module_args = {
+            "train_batch_size": self.train_batch_size,
+            "predict_batch_size": self.predict_batch_size,
+            "drop_incomplete_batch": self.drop_incomplete_batch,
+            "multiproc_kwargs": self.multiproc_kwargs,
+        }
+
+        nih_cxr14_re = get_dataset_from_module(
+            nih_cxr14_re_datamodule, stage, **module_args
+        )
+        padchest_no_tb = get_dataset_from_module(
+            padchest_no_tb_datamodule, stage, **module_args
+        )
+
+        self.dataset = {}
+        self.dataset["__train__"] = ConcatDataset(
+            [nih_cxr14_re["__train__"], padchest_no_tb["__train__"]]
+        )
+        self.dataset["train"] = ConcatDataset(
+            [nih_cxr14_re["train"], padchest_no_tb["train"]]
+        )
+        self.dataset["__valid__"] = ConcatDataset(
+            [nih_cxr14_re["__valid__"], padchest_no_tb["__valid__"]]
+        )
+        self.dataset["validation"] = ConcatDataset(
+            [nih_cxr14_re["validation"], padchest_no_tb["validation"]]
+        )
+        self.dataset["test"] = nih_cxr14_re["test"]
+
+        (
+            self.train_dataset,
+            self.validation_dataset,
+            self.extra_validation_datasets,
+            self.predict_dataset,
+        ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule