[data.hivtb] Minor adjustments

src/ptbench/data/hivtb/datamodule.py

@@ -11,7 +11,7 @@ from torchvision.transforms.functional import center_crop, to_tensor
 
 from ...utils.rc import load_rc
 from ..datamodule import CachingDataModule
-from ..image_utils import load_pil_grayscale, remove_black_borders
+from ..image_utils import remove_black_borders
 from ..split import JSONDatabaseSplit
 from ..typing import DatabaseSplit
 from ..typing import RawDataLoader as _BaseRawDataLoader
@@ -54,7 +54,9 @@ class RawDataLoader(_BaseRawDataLoader):
         sample
             The sample representation
         """
-        image = load_pil_grayscale(os.path.join(self.datadir, sample[0]))
+        image = PIL.Image.open(os.path.join(self.datadir, sample[0])).convert(
+            "L"
+        )
         image = remove_black_borders(image)
         tensor = to_tensor(image)
         tensor = center_crop(tensor, min(*tensor.shape[1:]))
@@ -99,21 +101,21 @@ class DataModule(CachingDataModule):
     """HIV-TB dataset for computer-aided diagnosis (only BMP files)
 
     * Database reference: [HIV-TB-2019]_
-    * Original resolution (height x width or width x height): 2048 x 2500 pixels
-      or 2500 x 2048 pixels
-    
+    * Original resolution, varying with most images being 2048 x 2500 pixels
+      or 2500 x 2048 pixels, but not all.
+
     Data specifications:
 
     * Raw data input (on disk):
 
-        * BMP images 8 bit grayscale
-        * resolution fixed to one of the cases above
+        * BMP (BMP3) and JPEG grayscale images encoded as 8-bit RGB, with
+          varying resolution
 
     * Output image:
 
         * Transforms:
 
-            * Load raw BMP with :py:mod:`PIL`
+            * Load raw BMP or JPEG with :py:mod:`PIL`
             * Remove black borders
             * Convert to torch tensor
             * Torch center cropping to get square image

src/ptbench/data/hivtb/fold_0.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 0)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-0.json")
+"""HIV-TB dataset for TB detection (cross validation fold 0).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_1.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 1)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-1.json")
+"""HIV-TB dataset for TB detection (cross validation fold 1).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_2.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 2)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-2.json")
+"""HIV-TB dataset for TB detection (cross validation fold 2).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_3.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 3)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-3.json")
+"""HIV-TB dataset for TB detection (cross validation fold 3).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_4.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 4)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-4.json")
+"""HIV-TB dataset for TB detection (cross validation fold 4).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_5.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 5)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-5.json")
+"""HIV-TB dataset for TB detection (cross validation fold 5).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_6.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 6)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-6.json")
+"""HIV-TB dataset for TB detection (cross validation fold 6).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_7.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 7)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-7.json")
+"""HIV-TB dataset for TB detection (cross validation fold 7).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_8.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 8)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-8.json")
+"""HIV-TB dataset for TB detection (cross validation fold 8).
 
+See :py:class:`DataModule` for technical details.
+"""

src/ptbench/data/hivtb/fold_9.py

@@ -2,20 +2,10 @@
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
 
-"""HIV-TB dataset for TB detection (cross validation fold 9)
-
-* Split reference: none (stratified kfolding)
-
-* Stratified kfold protocol:
-    * Training samples: 72% of TB and healthy CXR (including labels)
-    * Validation samples: 18% of TB and healthy CXR (including labels)
-    * Test samples: 10% of TB and healthy CXR (including labels)
-
-* This configuration resolution: 2048 x 2048 (default)
-* See :py:mod:`ptbench.data.hivtb` for dataset details
-"""
-
 from .datamodule import DataModule
 
 datamodule = DataModule("fold-9.json")
+"""HIV-TB dataset for TB detection (cross validation fold 9).
 
+See :py:class:`DataModule` for technical details.
+"""