Moved mc_ch_in_pc configs to data

pyproject.toml

@@ -465,8 +465,8 @@ hivtb_rs_f7 = "ptbench.configs.datasets.hivtb_RS.fold_7"
 hivtb_rs_f8 = "ptbench.configs.datasets.hivtb_RS.fold_8"
 hivtb_rs_f9 = "ptbench.configs.datasets.hivtb_RS.fold_9"
 # montgomery-shenzhen-indian-padchest aggregated dataset
-mc_ch_in_pc = "ptbench.configs.datasets.mc_ch_in_pc.default"
-mc_ch_in_pc_rgb = "ptbench.configs.datasets.mc_ch_in_pc.rgb"
+mc_ch_in_pc = "ptbench.data.mc_ch_in_pc.default"
+mc_ch_in_pc_rgb = "ptbench.data.mc_ch_in_pc.rgb"
 # extended montgomery-shenzhen-indian-padchest aggregated dataset
 # (with radiological signs)
 mc_ch_in_pc_rs = "ptbench.configs.datasets.mc_ch_in_pc_RS.default"

src/ptbench/configs/datasets/mc_ch_in_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 == "default":
-        from ..indian import default as indian
-        from ..montgomery import default as mc
-        from ..padchest import tb_idiap as pc
-        from ..shenzhen import default as ch
-    elif protocol == "rgb":
-        from ..indian import rgb as indian
-        from ..montgomery import rgb as mc
-        from ..padchest import tb_idiap_rgb as pc
-        from ..shenzhen import rgb as ch
-
-    mc = mc.dataset
-    ch = ch.dataset
-    indian = indian.dataset
-    pc = pc.dataset
-
-    dataset = {}
-    dataset["__train__"] = ConcatDataset(
-        [mc["__train__"], ch["__train__"], indian["__train__"], pc["__train__"]]
-    )
-    dataset["train"] = ConcatDataset(
-        [mc["train"], ch["train"], indian["train"], pc["train"]]
-    )
-    dataset["__valid__"] = ConcatDataset(
-        [mc["__valid__"], ch["__valid__"], indian["__valid__"], pc["__valid__"]]
-    )
-    dataset["test"] = ConcatDataset(
-        [mc["test"], ch["test"], indian["test"], pc["test"]]
-    )
-
-    return dataset

src/ptbench/configs/datasets/mc_ch_in_pc/default.py

-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest
-datasets."""
-
-from . import _maker
-
-dataset = _maker("default")

src/ptbench/configs/datasets/mc_ch_in_pc/rgb.py

-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest
-(RGB) datasets."""
-
-from . import _maker
-
-dataset = _maker("rgb")

src/ptbench/data/mc_ch_in_pc/__init__.py

+# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later

src/ptbench/data/mc_ch_in_pc/default.py

+# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest
+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 ..indian.default import datamodule as indian_datamodule
+from ..montgomery.default import datamodule as mc_datamodule
+from ..padchest.tb_idiap import datamodule as pc_datamodule
+from ..shenzhen.default import datamodule as ch_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):
+        # Instantiate other datamodules and get their datasets
+
+        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,
+        }
+
+        mc = get_dataset_from_module(mc_datamodule, stage, **module_args)
+        ch = get_dataset_from_module(ch_datamodule, stage, **module_args)
+        indian = get_dataset_from_module(
+            indian_datamodule, stage, **module_args
+        )
+        pc = get_dataset_from_module(pc_datamodule, stage, **module_args)
+
+        # Combine datasets
+        self.dataset = {}
+        self.dataset["__train__"] = ConcatDataset(
+            [
+                mc["__train__"],
+                ch["__train__"],
+                indian["__train__"],
+                pc["__train__"],
+            ]
+        )
+        self.dataset["train"] = ConcatDataset(
+            [mc["train"], ch["train"], indian["train"], pc["train"]]
+        )
+        self.dataset["__valid__"] = ConcatDataset(
+            [
+                mc["__valid__"],
+                ch["__valid__"],
+                indian["__valid__"],
+                pc["__valid__"],
+            ]
+        )
+        self.dataset["test"] = ConcatDataset(
+            [mc["test"], ch["test"], indian["test"], pc["test"]]
+        )
+
+        (
+            self.train_dataset,
+            self.validation_dataset,
+            self.extra_validation_datasets,
+            self.predict_dataset,
+        ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule

src/ptbench/data/mc_ch_in_pc/rgb.py

+# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+"""Aggregated dataset composed of Montgomery, Shenzhen, Indian and Padchest
+(RGB) 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 ..indian.rgb import datamodule as indian_datamodule
+from ..montgomery.rgb import datamodule as mc_datamodule
+from ..padchest.tb_idiap_rgb import datamodule as pc_datamodule
+from ..shenzhen.rgb import datamodule as ch_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):
+        # Instantiate other datamodules and get their datasets
+
+        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,
+        }
+
+        mc = get_dataset_from_module(mc_datamodule, stage, **module_args)
+        ch = get_dataset_from_module(ch_datamodule, stage, **module_args)
+        indian = get_dataset_from_module(
+            indian_datamodule, stage, **module_args
+        )
+        pc = get_dataset_from_module(pc_datamodule, stage, **module_args)
+
+        # Combine datasets
+        self.dataset = {}
+        self.dataset["__train__"] = ConcatDataset(
+            [
+                mc["__train__"],
+                ch["__train__"],
+                indian["__train__"],
+                pc["__train__"],
+            ]
+        )
+        self.dataset["train"] = ConcatDataset(
+            [mc["train"], ch["train"], indian["train"], pc["train"]]
+        )
+        self.dataset["__valid__"] = ConcatDataset(
+            [
+                mc["__valid__"],
+                ch["__valid__"],
+                indian["__valid__"],
+                pc["__valid__"],
+            ]
+        )
+        self.dataset["test"] = ConcatDataset(
+            [mc["test"], ch["test"], indian["test"], pc["test"]]
+        )
+
+        (
+            self.train_dataset,
+            self.validation_dataset,
+            self.extra_validation_datasets,
+            self.predict_dataset,
+        ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule