diff --git a/src/ptbench/data/mc_ch/__init__.py b/src/ptbench/data/mc_ch/__init__.py
deleted file mode 100644
index 662d5c1326651b4d9f48d47bc4b503df23d17216..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
diff --git a/src/ptbench/data/mc_ch/default.py b/src/ptbench/data/mc_ch/default.py
deleted file mode 100644
index cf901fdb41b14037c1827d364fedee1f70b1a665..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/default.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen 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 ..montgomery.default import datamodule as mc_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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_0.py b/src/ptbench/data/mc_ch/fold_0.py
deleted file mode 100644
index d3717d6fe5b821afd492528292a863676cd38e4c..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_0.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 0)"""
-
-
-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 ..montgomery.fold_0 import datamodule as mc_datamodule
-from ..shenzhen.fold_0 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_1.py b/src/ptbench/data/mc_ch/fold_1.py
deleted file mode 100644
index 2fb8136b648f89dbedae601fc1e3d5d7008926cd..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_1.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 1)"""
-
-
-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 ..montgomery.fold_1 import datamodule as mc_datamodule
-from ..shenzhen.fold_1 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_2.py b/src/ptbench/data/mc_ch/fold_2.py
deleted file mode 100644
index 4ef0dd55b40712cc17039fede1f3837e7e9c67b5..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_2.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 2)"""
-
-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 ..montgomery.fold_2 import datamodule as mc_datamodule
-from ..shenzhen.fold_2 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_3.py b/src/ptbench/data/mc_ch/fold_3.py
deleted file mode 100644
index 3c86f11ccaed5cb1fea8854d81fc704b28f4b2c1..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_3.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 3)"""
-
-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 ..montgomery.fold_3 import datamodule as mc_datamodule
-from ..shenzhen.fold_3 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_4.py b/src/ptbench/data/mc_ch/fold_4.py
deleted file mode 100644
index c0e085329a870315bee3dbdddfd58f83037fcd5d..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_4.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 4)"""
-
-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 ..montgomery.fold_4 import datamodule as mc_datamodule
-from ..shenzhen.fold_4 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_5.py b/src/ptbench/data/mc_ch/fold_5.py
deleted file mode 100644
index ca205eb3a84adc79899c64850f8abf5040206b90..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_5.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 5)"""
-
-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 ..montgomery.fold_5 import datamodule as mc_datamodule
-from ..shenzhen.fold_5 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_6.py b/src/ptbench/data/mc_ch/fold_6.py
deleted file mode 100644
index 42a6bb5812f0af7458dd2c0b791a12418f049921..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_6.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 6)"""
-
-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 ..montgomery.fold_6 import datamodule as mc_datamodule
-from ..shenzhen.fold_6 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_7.py b/src/ptbench/data/mc_ch/fold_7.py
deleted file mode 100644
index 082c0267b41abc67e30ee1db8c650adfe1582890..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_7.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 7)"""
-
-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 ..montgomery.fold_7 import datamodule as mc_datamodule
-from ..shenzhen.fold_7 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_8.py b/src/ptbench/data/mc_ch/fold_8.py
deleted file mode 100644
index 814fd9a41d7980984a4ccb854ba065a96d72dd37..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_8.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 8)"""
-
-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 ..montgomery.fold_8 import datamodule as mc_datamodule
-from ..shenzhen.fold_8 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/mc_ch/fold_9.py b/src/ptbench/data/mc_ch/fold_9.py
deleted file mode 100644
index a1d564e6d688416308cd897f947d4e99f7970e3b..0000000000000000000000000000000000000000
--- a/src/ptbench/data/mc_ch/fold_9.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery and Shenzhen datasets (cross
-validation fold 9)"""
-
-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 ..montgomery.fold_9 import datamodule as mc_datamodule
-from ..shenzhen.fold_9 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)
-
- # Combine datasets
- self.dataset = {}
- self.dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"]]
- )
- self.dataset["train"] = ConcatDataset([mc["train"], ch["train"]])
- self.dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"]]
- )
- self.dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"]]
- )
- self.dataset["test"] = ConcatDataset([mc["test"], ch["test"]])
-
- (
- self.train_dataset,
- self.validation_dataset,
- self.extra_validation_datasets,
- self.predict_dataset,
- ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
diff --git a/src/ptbench/data/montgomery_shenzhen/__init__.py b/src/ptbench/data/montgomery_shenzhen/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/ptbench/data/montgomery_shenzhen/datamodule.py b/src/ptbench/data/montgomery_shenzhen/datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..35ba9b9a04cace630fcced8aa15a7333ec4d7387
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/datamodule.py
@@ -0,0 +1,36 @@
+# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from ..datamodule import ConcatDataModule
+from ..montgomery.datamodule import RawDataLoader as MontgomeryLoader
+from ..montgomery.datamodule import make_split as make_montgomery_split
+from ..shenzhen.datamodule import RawDataLoader as ShenzhenLoader
+from ..shenzhen.datamodule import make_split as make_shenzhen_split
+
+
+class DataModule(ConcatDataModule):
+ """Aggregated datamodule composed of Montgomery and Shenzhen datasets."""
+
+ def __init__(self, split_filename: str):
+ montgomery_loader = MontgomeryLoader()
+ montgomery_split = make_montgomery_split("default.json")
+ shenzen_loader = ShenzhenLoader()
+ shenzen_split = make_shenzhen_split("default.json")
+
+ super().__init__(
+ splits={
+ "train": [
+ (montgomery_split["train"], montgomery_loader),
+ (shenzen_split["train"], shenzen_loader),
+ ],
+ "validation": [
+ (montgomery_split["validation"], montgomery_loader),
+ (shenzen_split["validation"], shenzen_loader),
+ ],
+ "test": [
+ (montgomery_split["test"], montgomery_loader),
+ (shenzen_split["test"], shenzen_loader),
+ ],
+ }
+ )
diff --git a/src/ptbench/data/montgomery_shenzhen/default.py b/src/ptbench/data/montgomery_shenzhen/default.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b8a8fb25e09b85f372e854a2f2ce29bcdf56ddc
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/default.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("default.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_0.py b/src/ptbench/data/montgomery_shenzhen/fold_0.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d114d07d1c53f869ba710074b254944865cc4bc
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_0.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_0.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_1.py b/src/ptbench/data/montgomery_shenzhen/fold_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd3a8cb6d0be1c01e5dbb96d5cc02f724e15e7c4
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_1.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_1.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_2.py b/src/ptbench/data/montgomery_shenzhen/fold_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..44eeda801884db9fcaa58be04de3068ff82325eb
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_2.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_2.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_3.py b/src/ptbench/data/montgomery_shenzhen/fold_3.py
new file mode 100644
index 0000000000000000000000000000000000000000..f24fb314e30df9ab111ac69fd74a7f9a16cdad68
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_3.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_3.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_4.py b/src/ptbench/data/montgomery_shenzhen/fold_4.py
new file mode 100644
index 0000000000000000000000000000000000000000..58456d38e4804c2b68e8759f78e3bf9ea8d547f8
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_4.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_4.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_5.py b/src/ptbench/data/montgomery_shenzhen/fold_5.py
new file mode 100644
index 0000000000000000000000000000000000000000..927967469ff479647670fe0d0978e4b72b965c33
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_5.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_5.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_6.py b/src/ptbench/data/montgomery_shenzhen/fold_6.py
new file mode 100644
index 0000000000000000000000000000000000000000..9566b7cfe5723c1da18d081a2442fb5d13a11d3e
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_6.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_6.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_7.py b/src/ptbench/data/montgomery_shenzhen/fold_7.py
new file mode 100644
index 0000000000000000000000000000000000000000..25cbfe1b2eb0290098e6b54c3ca5a26076482e0c
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_7.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_7.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_8.py b/src/ptbench/data/montgomery_shenzhen/fold_8.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb5332ce050631a6fdf4b590fb4dd5a191fd90bc
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_8.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_8.json")
diff --git a/src/ptbench/data/montgomery_shenzhen/fold_9.py b/src/ptbench/data/montgomery_shenzhen/fold_9.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1626586ff8b263c097ee9b929eea40c42b4ae1e
--- /dev/null
+++ b/src/ptbench/data/montgomery_shenzhen/fold_9.py
@@ -0,0 +1,7 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+from .datamodule import DataModule
+
+datamodule = DataModule("fold_9.json")