removed unusable tbx11k custom split 3

doc/api.rst

@@ -57,8 +57,6 @@ Direct data-access through iterators.
    ptbench.data.tbx11k_simplified_RS
    ptbench.data.tbx11k_simplified_v2
    ptbench.data.tbx11k_simplified_v2_RS
-   ptbench.data.tbx11k_simplified_v3
-   ptbench.data.tbx11k_simplified_v3_RS
 
 
 .. _ptbench.api.models:

doc/config.rst

@@ -80,9 +80,6 @@ if applicable.  Use these datasets for training and evaluating your models.
    ptbench.configs.datasets.tbx11k_simplified_v2.default
    ptbench.configs.datasets.tbx11k_simplified_v2.rgb
    ptbench.configs.datasets.tbx11k_simplified_v2_RS.default
-   ptbench.configs.datasets.tbx11k_simplified_v3.default
-   ptbench.configs.datasets.tbx11k_simplified_v3.rgb
-   ptbench.configs.datasets.tbx11k_simplified_v3_RS.default
 
 
 .. _ptbench.configs.datasets.folds:
@@ -133,9 +130,6 @@ datasets.  Nine other folds are available for every configuration (from 1 to
    ptbench.configs.datasets.tbx11k_simplified_v2.fold_0
    ptbench.configs.datasets.tbx11k_simplified_v2.fold_0_rgb
    ptbench.configs.datasets.tbx11k_simplified_v2_RS.fold_0
-   ptbench.configs.datasets.tbx11k_simplified_v3.fold_0
-   ptbench.configs.datasets.tbx11k_simplified_v3.fold_0_rgb
-   ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_0
 
 
 .. include:: links.rst

pyproject.toml

@@ -256,41 +256,6 @@ tbx11k_simplified_v2_rs_f6 = "ptbench.configs.datasets.tbx11k_simplified_v2_RS.f
 tbx11k_simplified_v2_rs_f7 = "ptbench.configs.datasets.tbx11k_simplified_v2_RS.fold_7"
 tbx11k_simplified_v2_rs_f8 = "ptbench.configs.datasets.tbx11k_simplified_v2_RS.fold_8"
 tbx11k_simplified_v2_rs_f9 = "ptbench.configs.datasets.tbx11k_simplified_v2_RS.fold_9"
-# TBX11K simplified dataset split 3 (and cross-validation folds)
-tbx11k_simplified_v3 = "ptbench.configs.datasets.tbx11k_simplified_v3.default"
-tbx11k_simplified_v3_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.rgb"
-tbx11k_simplified_v3_f0 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_0"
-tbx11k_simplified_v3_f1 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_1"
-tbx11k_simplified_v3_f2 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_2"
-tbx11k_simplified_v3_f3 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_3"
-tbx11k_simplified_v3_f4 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_4"
-tbx11k_simplified_v3_f5 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_5"
-tbx11k_simplified_v3_f6 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_6"
-tbx11k_simplified_v3_f7 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_7"
-tbx11k_simplified_v3_f8 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_8"
-tbx11k_simplified_v3_f9 = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_9"
-tbx11k_simplified_v3_f0_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_0_rgb"
-tbx11k_simplified_v3_f1_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_1_rgb"
-tbx11k_simplified_v3_f2_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_2_rgb"
-tbx11k_simplified_v3_f3_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_3_rgb"
-tbx11k_simplified_v3_f4_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_4_rgb"
-tbx11k_simplified_v3_f5_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_5_rgb"
-tbx11k_simplified_v3_f6_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_6_rgb"
-tbx11k_simplified_v3_f7_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_7_rgb"
-tbx11k_simplified_v3_f8_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_8_rgb"
-tbx11k_simplified_v3_f9_rgb = "ptbench.configs.datasets.tbx11k_simplified_v3.fold_9_rgb"
-# extended TBX11K simplified dataset split 3 (with radiological signs)
-tbx11k_simplified_v3_rs = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.default"
-tbx11k_simplified_v3_rs_f0 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_0"
-tbx11k_simplified_v3_rs_f1 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_1"
-tbx11k_simplified_v3_rs_f2 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_2"
-tbx11k_simplified_v3_rs_f3 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_3"
-tbx11k_simplified_v3_rs_f4 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_4"
-tbx11k_simplified_v3_rs_f5 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_5"
-tbx11k_simplified_v3_rs_f6 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_6"
-tbx11k_simplified_v3_rs_f7 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_7"
-tbx11k_simplified_v3_rs_f8 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_8"
-tbx11k_simplified_v3_rs_f9 = "ptbench.configs.datasets.tbx11k_simplified_v3_RS.fold_9"
 # montgomery-shenzhen aggregated dataset
 mc_ch = "ptbench.configs.datasets.mc_ch.default"
 mc_ch_rgb = "ptbench.configs.datasets.mc_ch.rgb"

src/ptbench/configs/datasets/tbx11k_simplified_v3/__init__.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-
-def _maker(protocol, RGB=False):
-    from torchvision import transforms
-
-    from ....data.tbx11k_simplified_v3 import dataset as raw
-    from ....data.transforms import ElasticDeformation
-    from .. import make_dataset as mk
-
-    post_transforms = []
-    if RGB:
-        post_transforms = [
-            transforms.Lambda(lambda x: x.convert("RGB")),
-            transforms.ToTensor(),
-        ]
-
-    return mk(
-        [raw.subsets(protocol)],
-        [],
-        [ElasticDeformation(p=0.8)],
-        post_transforms,
-    )

src/ptbench/configs/datasets/tbx11k_simplified_v3/default.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (default protocol)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("default")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_0.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 0)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_0")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_0_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 0, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_0", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_1.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 1)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_1")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_1_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 1, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_1", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_2.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 2)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_2")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_2_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 2, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_2", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_3.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 3)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_3")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_3_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 3, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_3", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_4.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 4)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_4")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_4_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 4, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_4", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_5.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 5)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_5")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_5_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 5, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_5", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_6.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 6)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_6")

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_6_rgb.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 6, RGB)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_6", RGB=True)

src/ptbench/configs/datasets/tbx11k_simplified_v3/fold_7.py

-# Copyright © 2022 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-
-"""TBX11k simplified dataset for TB detection (cross validation fold 7)
-
-* Split reference: first 62.6% of CXR for "train", 16% for "validation",
-* 21.4% for "test"
-* This split consists of the 4 labels "healthy", "latent TB", "sick & non-TB",
-* and "active TB"
-* This configuration resolution: 512 x 512 (default)
-* See :py:mod:`ptbench.data.tbx11k_v3` for dataset details
-"""
-
-from . import _maker
-
-dataset = _maker("fold_7")