From 04351e194cf5817866dc0320cc3b417bf0f8baef Mon Sep 17 00:00:00 2001
From: dcarron <daniel.carron@idiap.ch>
Date: Mon, 5 Jun 2023 16:18:05 +0200
Subject: [PATCH] Moved mc_ch_in configs to data
---
pyproject.toml | 44 +++----
.../configs/datasets/mc_ch_in/__init__.py | 117 ------------------
.../configs/datasets/mc_ch_in/default.py | 9 --
.../configs/datasets/mc_ch_in/fold_0.py | 10 --
.../configs/datasets/mc_ch_in/fold_0_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_1.py | 10 --
.../configs/datasets/mc_ch_in/fold_1_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_2.py | 10 --
.../configs/datasets/mc_ch_in/fold_2_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_3.py | 10 --
.../configs/datasets/mc_ch_in/fold_3_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_4.py | 10 --
.../configs/datasets/mc_ch_in/fold_4_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_5.py | 10 --
.../configs/datasets/mc_ch_in/fold_5_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_6.py | 10 --
.../configs/datasets/mc_ch_in/fold_6_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_7.py | 10 --
.../configs/datasets/mc_ch_in/fold_7_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_8.py | 10 --
.../configs/datasets/mc_ch_in/fold_8_rgb.py | 10 --
.../configs/datasets/mc_ch_in/fold_9.py | 10 --
.../configs/datasets/mc_ch_in/fold_9_rgb.py | 10 --
src/ptbench/configs/datasets/mc_ch_in/rgb.py | 10 --
src/ptbench/data/mc_ch_in/__init__.py | 3 +
src/ptbench/data/mc_ch_in/default.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_0.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_0_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_1.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_1_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_2.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_2_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_3.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_3_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_4.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_4_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_5.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_5_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_6.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_6_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_7.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_7_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_8.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_8_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_9.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/fold_9_rgb.py | 81 ++++++++++++
src/ptbench/data/mc_ch_in/rgb.py | 81 ++++++++++++
47 files changed, 1807 insertions(+), 358 deletions(-)
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/__init__.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/default.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_0.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_0_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_1.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_1_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_2.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_2_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_3.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_3_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_4.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_4_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_5.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_5_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_6.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_6_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_7.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_7_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_8.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_8_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_9.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/fold_9_rgb.py
delete mode 100644 src/ptbench/configs/datasets/mc_ch_in/rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/__init__.py
create mode 100644 src/ptbench/data/mc_ch_in/default.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_0.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_0_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_1.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_1_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_2.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_2_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_3.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_3_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_4.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_4_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_5.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_5_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_6.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_6_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_7.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_7_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_8.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_8_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_9.py
create mode 100644 src/ptbench/data/mc_ch_in/fold_9_rgb.py
create mode 100644 src/ptbench/data/mc_ch_in/rgb.py
diff --git a/pyproject.toml b/pyproject.toml
index 67211c83..b936dddb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -293,28 +293,28 @@ mc_ch_rs_f7 = "ptbench.configs.datasets.mc_ch_RS.fold_7"
mc_ch_rs_f8 = "ptbench.configs.datasets.mc_ch_RS.fold_8"
mc_ch_rs_f9 = "ptbench.configs.datasets.mc_ch_RS.fold_9"
# montgomery-shenzhen-indian aggregated dataset
-mc_ch_in = "ptbench.configs.datasets.mc_ch_in.default"
-mc_ch_in_rgb = "ptbench.configs.datasets.mc_ch_in.rgb"
-mc_ch_in_f0 = "ptbench.configs.datasets.mc_ch_in.fold_0"
-mc_ch_in_f1 = "ptbench.configs.datasets.mc_ch_in.fold_1"
-mc_ch_in_f2 = "ptbench.configs.datasets.mc_ch_in.fold_2"
-mc_ch_in_f3 = "ptbench.configs.datasets.mc_ch_in.fold_3"
-mc_ch_in_f4 = "ptbench.configs.datasets.mc_ch_in.fold_4"
-mc_ch_in_f5 = "ptbench.configs.datasets.mc_ch_in.fold_5"
-mc_ch_in_f6 = "ptbench.configs.datasets.mc_ch_in.fold_6"
-mc_ch_in_f7 = "ptbench.configs.datasets.mc_ch_in.fold_7"
-mc_ch_in_f8 = "ptbench.configs.datasets.mc_ch_in.fold_8"
-mc_ch_in_f9 = "ptbench.configs.datasets.mc_ch_in.fold_9"
-mc_ch_in_f0_rgb = "ptbench.configs.datasets.mc_ch_in.fold_0_rgb"
-mc_ch_in_f1_rgb = "ptbench.configs.datasets.mc_ch_in.fold_1_rgb"
-mc_ch_in_f2_rgb = "ptbench.configs.datasets.mc_ch_in.fold_2_rgb"
-mc_ch_in_f3_rgb = "ptbench.configs.datasets.mc_ch_in.fold_3_rgb"
-mc_ch_in_f4_rgb = "ptbench.configs.datasets.mc_ch_in.fold_4_rgb"
-mc_ch_in_f5_rgb = "ptbench.configs.datasets.mc_ch_in.fold_5_rgb"
-mc_ch_in_f6_rgb = "ptbench.configs.datasets.mc_ch_in.fold_6_rgb"
-mc_ch_in_f7_rgb = "ptbench.configs.datasets.mc_ch_in.fold_7_rgb"
-mc_ch_in_f8_rgb = "ptbench.configs.datasets.mc_ch_in.fold_8_rgb"
-mc_ch_in_f9_rgb = "ptbench.configs.datasets.mc_ch_in.fold_9_rgb"
+mc_ch_in = "ptbench.data.mc_ch_in.default"
+mc_ch_in_rgb = "ptbench.data.mc_ch_in.rgb"
+mc_ch_in_f0 = "ptbench.data.mc_ch_in.fold_0"
+mc_ch_in_f1 = "ptbench.data.mc_ch_in.fold_1"
+mc_ch_in_f2 = "ptbench.data.mc_ch_in.fold_2"
+mc_ch_in_f3 = "ptbench.data.mc_ch_in.fold_3"
+mc_ch_in_f4 = "ptbench.data.mc_ch_in.fold_4"
+mc_ch_in_f5 = "ptbench.data.mc_ch_in.fold_5"
+mc_ch_in_f6 = "ptbench.data.mc_ch_in.fold_6"
+mc_ch_in_f7 = "ptbench.data.mc_ch_in.fold_7"
+mc_ch_in_f8 = "ptbench.data.mc_ch_in.fold_8"
+mc_ch_in_f9 = "ptbench.data.mc_ch_in.fold_9"
+mc_ch_in_f0_rgb = "ptbench.data.mc_ch_in.fold_0_rgb"
+mc_ch_in_f1_rgb = "ptbench.data.mc_ch_in.fold_1_rgb"
+mc_ch_in_f2_rgb = "ptbench.data.mc_ch_in.fold_2_rgb"
+mc_ch_in_f3_rgb = "ptbench.data.mc_ch_in.fold_3_rgb"
+mc_ch_in_f4_rgb = "ptbench.data.mc_ch_in.fold_4_rgb"
+mc_ch_in_f5_rgb = "ptbench.data.mc_ch_in.fold_5_rgb"
+mc_ch_in_f6_rgb = "ptbench.data.mc_ch_in.fold_6_rgb"
+mc_ch_in_f7_rgb = "ptbench.data.mc_ch_in.fold_7_rgb"
+mc_ch_in_f8_rgb = "ptbench.data.mc_ch_in.fold_8_rgb"
+mc_ch_in_f9_rgb = "ptbench.data.mc_ch_in.fold_9_rgb"
# extended montgomery-shenzhen-indian aggregated dataset
# (with radiological signs)
mc_ch_in_rs = "ptbench.configs.datasets.mc_ch_in_RS.default"
diff --git a/src/ptbench/configs/datasets/mc_ch_in/__init__.py b/src/ptbench/configs/datasets/mc_ch_in/__init__.py
deleted file mode 100644
index 41023bb3..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/__init__.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# 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 ..shenzhen import default as ch
- elif protocol == "rgb":
- from ..indian import rgb as indian
- from ..montgomery import rgb as mc
- from ..shenzhen import rgb as ch
- elif protocol == "fold_0":
- from ..indian import fold_0 as indian
- from ..montgomery import fold_0 as mc
- from ..shenzhen import fold_0 as ch
- elif protocol == "fold_1":
- from ..indian import fold_1 as indian
- from ..montgomery import fold_1 as mc
- from ..shenzhen import fold_1 as ch
- elif protocol == "fold_2":
- from ..indian import fold_2 as indian
- from ..montgomery import fold_2 as mc
- from ..shenzhen import fold_2 as ch
- elif protocol == "fold_3":
- from ..indian import fold_3 as indian
- from ..montgomery import fold_3 as mc
- from ..shenzhen import fold_3 as ch
- elif protocol == "fold_4":
- from ..indian import fold_4 as indian
- from ..montgomery import fold_4 as mc
- from ..shenzhen import fold_4 as ch
- elif protocol == "fold_5":
- from ..indian import fold_5 as indian
- from ..montgomery import fold_5 as mc
- from ..shenzhen import fold_5 as ch
- elif protocol == "fold_6":
- from ..indian import fold_6 as indian
- from ..montgomery import fold_6 as mc
- from ..shenzhen import fold_6 as ch
- elif protocol == "fold_7":
- from ..indian import fold_7 as indian
- from ..montgomery import fold_7 as mc
- from ..shenzhen import fold_7 as ch
- elif protocol == "fold_8":
- from ..indian import fold_8 as indian
- from ..montgomery import fold_8 as mc
- from ..shenzhen import fold_8 as ch
- elif protocol == "fold_9":
- from ..indian import fold_9 as indian
- from ..montgomery import fold_9 as mc
- from ..shenzhen import fold_9 as ch
- elif protocol == "fold_0_rgb":
- from ..indian import fold_0_rgb as indian
- from ..montgomery import fold_0_rgb as mc
- from ..shenzhen import fold_0_rgb as ch
- elif protocol == "fold_1_rgb":
- from ..indian import fold_1_rgb as indian
- from ..montgomery import fold_1_rgb as mc
- from ..shenzhen import fold_1_rgb as ch
- elif protocol == "fold_2_rgb":
- from ..indian import fold_2_rgb as indian
- from ..montgomery import fold_2_rgb as mc
- from ..shenzhen import fold_2_rgb as ch
- elif protocol == "fold_3_rgb":
- from ..indian import fold_3_rgb as indian
- from ..montgomery import fold_3_rgb as mc
- from ..shenzhen import fold_3_rgb as ch
- elif protocol == "fold_4_rgb":
- from ..indian import fold_4_rgb as indian
- from ..montgomery import fold_4_rgb as mc
- from ..shenzhen import fold_4_rgb as ch
- elif protocol == "fold_5_rgb":
- from ..indian import fold_5_rgb as indian
- from ..montgomery import fold_5_rgb as mc
- from ..shenzhen import fold_5_rgb as ch
- elif protocol == "fold_6_rgb":
- from ..indian import fold_6_rgb as indian
- from ..montgomery import fold_6_rgb as mc
- from ..shenzhen import fold_6_rgb as ch
- elif protocol == "fold_7_rgb":
- from ..indian import fold_7_rgb as indian
- from ..montgomery import fold_7_rgb as mc
- from ..shenzhen import fold_7_rgb as ch
- elif protocol == "fold_8_rgb":
- from ..indian import fold_8_rgb as indian
- from ..montgomery import fold_8_rgb as mc
- from ..shenzhen import fold_8_rgb as ch
- elif protocol == "fold_9_rgb":
- from ..indian import fold_9_rgb as indian
- from ..montgomery import fold_9_rgb as mc
- from ..shenzhen import fold_9_rgb as ch
-
- mc = mc.dataset
- ch = ch.dataset
- indian = indian.dataset
-
- dataset = {}
- dataset["__train__"] = ConcatDataset(
- [mc["__train__"], ch["__train__"], indian["__train__"]]
- )
- dataset["train"] = ConcatDataset(
- [mc["train"], ch["train"], indian["train"]]
- )
- dataset["__valid__"] = ConcatDataset(
- [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
- )
- dataset["validation"] = ConcatDataset(
- [mc["validation"], ch["validation"], indian["validation"]]
- )
- dataset["test"] = ConcatDataset([mc["test"], ch["test"], indian["test"]])
-
- return dataset
diff --git a/src/ptbench/configs/datasets/mc_ch_in/default.py b/src/ptbench/configs/datasets/mc_ch_in/default.py
deleted file mode 100644
index 8408ffb2..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/default.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets."""
-
-from . import _maker
-
-dataset = _maker("default")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_0.py b/src/ptbench/configs/datasets/mc_ch_in/fold_0.py
deleted file mode 100644
index 405bb426..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_0.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 0)"""
-
-from . import _maker
-
-dataset = _maker("fold_0")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_0_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_0_rgb.py
deleted file mode 100644
index 9ff3224a..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_0_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 0, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_0_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_1.py b/src/ptbench/configs/datasets/mc_ch_in/fold_1.py
deleted file mode 100644
index 2d3c5fad..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_1.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 1)"""
-
-from . import _maker
-
-dataset = _maker("fold_1")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_1_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_1_rgb.py
deleted file mode 100644
index b478b75b..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_1_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 1, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_1_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_2.py b/src/ptbench/configs/datasets/mc_ch_in/fold_2.py
deleted file mode 100644
index d726858c..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_2.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 2)"""
-
-from . import _maker
-
-dataset = _maker("fold_2")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_2_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_2_rgb.py
deleted file mode 100644
index 0cf81050..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_2_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 2, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_2_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_3.py b/src/ptbench/configs/datasets/mc_ch_in/fold_3.py
deleted file mode 100644
index 92e1ac8d..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_3.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 3)"""
-
-from . import _maker
-
-dataset = _maker("fold_3")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_3_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_3_rgb.py
deleted file mode 100644
index 23651bb0..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_3_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 3, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_3_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_4.py b/src/ptbench/configs/datasets/mc_ch_in/fold_4.py
deleted file mode 100644
index 6e3aaa3c..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_4.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 4)"""
-
-from . import _maker
-
-dataset = _maker("fold_4")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_4_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_4_rgb.py
deleted file mode 100644
index 9addb86a..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_4_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 4, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_4_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_5.py b/src/ptbench/configs/datasets/mc_ch_in/fold_5.py
deleted file mode 100644
index edae2bae..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_5.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 5)"""
-
-from . import _maker
-
-dataset = _maker("fold_5")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_5_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_5_rgb.py
deleted file mode 100644
index 20a0b324..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_5_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 5, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_5_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_6.py b/src/ptbench/configs/datasets/mc_ch_in/fold_6.py
deleted file mode 100644
index 5ae1c3cc..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_6.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 6)"""
-
-from . import _maker
-
-dataset = _maker("fold_6")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_6_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_6_rgb.py
deleted file mode 100644
index 874057b3..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_6_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 6, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_6_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_7.py b/src/ptbench/configs/datasets/mc_ch_in/fold_7.py
deleted file mode 100644
index 5ab352c6..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_7.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 7)"""
-
-from . import _maker
-
-dataset = _maker("fold_7")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_7_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_7_rgb.py
deleted file mode 100644
index 5014ff5b..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_7_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 7, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_7_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_8.py b/src/ptbench/configs/datasets/mc_ch_in/fold_8.py
deleted file mode 100644
index 49ec1c40..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_8.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 8)"""
-
-from . import _maker
-
-dataset = _maker("fold_8")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_8_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_8_rgb.py
deleted file mode 100644
index deb1e4a9..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_8_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 8, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_8_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_9.py b/src/ptbench/configs/datasets/mc_ch_in/fold_9.py
deleted file mode 100644
index b701a9c8..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_9.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 9)"""
-
-from . import _maker
-
-dataset = _maker("fold_9")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/fold_9_rgb.py b/src/ptbench/configs/datasets/mc_ch_in/fold_9_rgb.py
deleted file mode 100644
index a6b3b43b..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/fold_9_rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian datasets
-(cross validation fold 9, RGB)"""
-
-from . import _maker
-
-dataset = _maker("fold_9_rgb")
diff --git a/src/ptbench/configs/datasets/mc_ch_in/rgb.py b/src/ptbench/configs/datasets/mc_ch_in/rgb.py
deleted file mode 100644
index 5f0d4757..00000000
--- a/src/ptbench/configs/datasets/mc_ch_in/rgb.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""Aggregated dataset composed of Montgomery, Shenzhen and Indian (RGB)
-datasets."""
-
-from . import _maker
-
-dataset = _maker("rgb")
diff --git a/src/ptbench/data/mc_ch_in/__init__.py b/src/ptbench/data/mc_ch_in/__init__.py
new file mode 100644
index 00000000..662d5c13
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/__init__.py
@@ -0,0 +1,3 @@
+# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
diff --git a/src/ptbench/data/mc_ch_in/default.py b/src/ptbench/data/mc_ch_in/default.py
new file mode 100644
index 00000000..7d2d6fc0
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/default.py
@@ -0,0 +1,81 @@
+# 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 ..indian.default import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_0.py b/src/ptbench/data/mc_ch_in/fold_0.py
new file mode 100644
index 00000000..66e9e07e
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_0.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_0 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_0_rgb.py b/src/ptbench/data/mc_ch_in/fold_0_rgb.py
new file mode 100644
index 00000000..bb4b7fc1
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_0_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_0_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_0_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_0_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_1.py b/src/ptbench/data/mc_ch_in/fold_1.py
new file mode 100644
index 00000000..d98c097e
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_1.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_1 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_1_rgb.py b/src/ptbench/data/mc_ch_in/fold_1_rgb.py
new file mode 100644
index 00000000..32a94a5d
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_1_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_1_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_1_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_1_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_2.py b/src/ptbench/data/mc_ch_in/fold_2.py
new file mode 100644
index 00000000..15eaf1aa
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_2.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_2 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_2_rgb.py b/src/ptbench/data/mc_ch_in/fold_2_rgb.py
new file mode 100644
index 00000000..4582172f
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_2_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_2_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_2_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_2_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_3.py b/src/ptbench/data/mc_ch_in/fold_3.py
new file mode 100644
index 00000000..54b8e1c5
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_3.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_3 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_3_rgb.py b/src/ptbench/data/mc_ch_in/fold_3_rgb.py
new file mode 100644
index 00000000..bcc22dad
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_3_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_3_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_3_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_3_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_4.py b/src/ptbench/data/mc_ch_in/fold_4.py
new file mode 100644
index 00000000..a3dde801
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_4.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_4 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_4_rgb.py b/src/ptbench/data/mc_ch_in/fold_4_rgb.py
new file mode 100644
index 00000000..ee076ac0
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_4_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_4_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_4_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_4_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_5.py b/src/ptbench/data/mc_ch_in/fold_5.py
new file mode 100644
index 00000000..dcbf4fbb
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_5.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_5 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_5_rgb.py b/src/ptbench/data/mc_ch_in/fold_5_rgb.py
new file mode 100644
index 00000000..660037c6
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_5_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_5_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_5_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_5_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_6.py b/src/ptbench/data/mc_ch_in/fold_6.py
new file mode 100644
index 00000000..20a797cb
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_6.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_6 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_6_rgb.py b/src/ptbench/data/mc_ch_in/fold_6_rgb.py
new file mode 100644
index 00000000..a90cbfea
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_6_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_6_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_6_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_6_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_7.py b/src/ptbench/data/mc_ch_in/fold_7.py
new file mode 100644
index 00000000..086f2503
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_7.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_7 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_7_rgb.py b/src/ptbench/data/mc_ch_in/fold_7_rgb.py
new file mode 100644
index 00000000..b8efe821
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_7_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_7_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_7_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_7_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_8.py b/src/ptbench/data/mc_ch_in/fold_8.py
new file mode 100644
index 00000000..a02325aa
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_8.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_8 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_8_rgb.py b/src/ptbench/data/mc_ch_in/fold_8_rgb.py
new file mode 100644
index 00000000..190440f5
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_8_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_8_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_8_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_8_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_9.py b/src/ptbench/data/mc_ch_in/fold_9.py
new file mode 100644
index 00000000..bf14f21f
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_9.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_9 import datamodule as indian_datamodule
+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)
+ indian = get_dataset_from_module(
+ indian_datamodule, stage, **module_args
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/fold_9_rgb.py b/src/ptbench/data/mc_ch_in/fold_9_rgb.py
new file mode 100644
index 00000000..7a7bc632
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/fold_9_rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.fold_9_rgb import datamodule as indian_datamodule
+from ..montgomery.fold_9_rgb import datamodule as mc_datamodule
+from ..shenzhen.fold_9_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["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_in/rgb.py b/src/ptbench/data/mc_ch_in/rgb.py
new file mode 100644
index 00000000..e10748b2
--- /dev/null
+++ b/src/ptbench/data/mc_ch_in/rgb.py
@@ -0,0 +1,81 @@
+# 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 ..indian.rgb import datamodule as indian_datamodule
+from ..montgomery.rgb import datamodule as mc_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
+ )
+
+ # Combine datasets
+ self.dataset = {}
+ self.dataset["__train__"] = ConcatDataset(
+ [mc["__train__"], ch["__train__"], indian["__train__"]]
+ )
+ self.dataset["train"] = ConcatDataset(
+ [mc["train"], ch["train"], indian["train"]]
+ )
+ self.dataset["__valid__"] = ConcatDataset(
+ [mc["__valid__"], ch["__valid__"], indian["__valid__"]]
+ )
+ self.dataset["validation"] = ConcatDataset(
+ [mc["validation"], ch["validation"], indian["validation"]]
+ )
+ self.dataset["test"] = ConcatDataset(
+ [mc["test"], ch["test"], indian["test"]]
+ )
+
+ (
+ self.train_dataset,
+ self.validation_dataset,
+ self.extra_validation_datasets,
+ self.predict_dataset,
+ ) = return_subsets(self.dataset)
+
+
+datamodule = DefaultModule
--
GitLab