From 06cef603f5c997457e15e44b7b184e04cbdcc323 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Wed, 2 Aug 2023 10:52:21 +0200
Subject: [PATCH] [data.padchest] Port padchest to new lightning infrastructure

---
 .pre-commit-config.yaml                       |   7 +-
 pyproject.toml                                |  14 +-
 src/ptbench/data/datamodule.py                |   5 +
 src/ptbench/data/image_utils.py               |  42 +--
 src/ptbench/data/nih_cxr14/__init__.py        |  97 -----
 src/ptbench/data/padchest/__init__.py         | 281 ---------------
 .../data/padchest/cardiomegaly_idiap.json     | 164 +++++++++
 .../data/padchest/cardiomegaly_idiap.json.bz2 | Bin 1186 -> 0 bytes
 .../data/padchest/cardiomegaly_idiap.py       |  48 +--
 src/ptbench/data/padchest/datamodule.py       | 333 ++++++++++++++++++
 src/ptbench/data/padchest/idiap.json.bz2      | Bin 1462333 -> 1436734 bytes
 src/ptbench/data/padchest/idiap.py            |  47 +--
 .../data/padchest/no-tb-idiap.json.bz2        | Bin 0 -> 738435 bytes
 .../data/padchest/no_tb_idiap.json.bz2        | Bin 737640 -> 0 bytes
 src/ptbench/data/padchest/no_tb_idiap.py      |  64 ++--
 src/ptbench/data/padchest/tb-idiap.json       | 256 ++++++++++++++
 src/ptbench/data/padchest/tb_idiap.json.bz2   | Bin 5599 -> 0 bytes
 src/ptbench/data/padchest/tb_idiap.py         |  49 +--
 18 files changed, 826 insertions(+), 581 deletions(-)
 create mode 100644 src/ptbench/data/padchest/cardiomegaly_idiap.json
 delete mode 100644 src/ptbench/data/padchest/cardiomegaly_idiap.json.bz2
 create mode 100644 src/ptbench/data/padchest/datamodule.py
 create mode 100644 src/ptbench/data/padchest/no-tb-idiap.json.bz2
 delete mode 100644 src/ptbench/data/padchest/no_tb_idiap.json.bz2
 create mode 100644 src/ptbench/data/padchest/tb-idiap.json
 delete mode 100644 src/ptbench/data/padchest/tb_idiap.json.bz2

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index b7818c75..0fc301b3 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -49,11 +49,8 @@ repos:
       - id: check-added-large-files
         exclude: |
           (?x)(
-              ^src/ptbench/data/nih_cxr14_re/idiap.json|
-              ^src/ptbench/data/nih_cxr14_re/default.json|
-              ^src/ptbench/data/padchest/idiap.json|
-              ^src/ptbench/data/padchest/no_tb_idiap.json|
-              ^src/ptbench/data/padchest/no_tb_idiap.json|
+              ^src/ptbench/data/padchest/idiap.json.bz2|
+              ^src/ptbench/data/padchest/no-tb-idiap.json.bz2|
               ^tests/data/16bits.png|
               ^doc/results/img/rad_sign_drop.png
               )
diff --git a/pyproject.toml b/pyproject.toml
index ff03e366..23a5908c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -220,19 +220,19 @@ hivtb_f9 = "ptbench.data.hivtb.fold_9"
 # montgomery-shenzhen-indian-padchest aggregated dataset
 mc_ch_in_pc = "ptbench.data.mc_ch_in_pc.default"
 
-# NIH CXR14 (relabeled)
+# NIH CXR14 (relabeled), multi-class (14 labels)
 nih-cxr14 = "ptbench.data.nih_cxr14.default"
 nih-cxr14-cardiomegaly = "ptbench.data.nih_cxr14.cardiomegaly"
 
+# PadChest, multi-class (varied number of labels)
+padchest-idiap = "ptbench.data.padchest.idiap"
+padchest-tb-idiap = "ptbench.data.padchest.tb_idiap"
+padchest-no-tb-idiap = "ptbench.data.padchest.no_tb_idiap"
+padchest-cardiomegaly-idiap = "ptbench.data.padchest.cardiomegaly_idiap"
+
 # NIH CXR14 / PadChest aggregated dataset
 nih_cxr14_pc_idiap = "ptbench.data.nih_cxr14_re_pc.idiap"
 
-# PadChest
-padchest_idiap = "ptbench.data.padchest.idiap"
-padchest_tb_idiap = "ptbench.data.padchest.tb_idiap"
-padchest_no_tb_idiap = "ptbench.data.padchest.no_tb_idiap"
-padchest_cm_idiap = "ptbench.data.padchest.cardiomegaly_idiap"
-
 [tool.setuptools]
 zip-safe = true
 package-dir = { "" = "src" }
diff --git a/src/ptbench/data/datamodule.py b/src/ptbench/data/datamodule.py
index 9b5eab61..0785e233 100644
--- a/src/ptbench/data/datamodule.py
+++ b/src/ptbench/data/datamodule.py
@@ -541,6 +541,11 @@ class ConcatDataModule(lightning.LightningDataModule):
         self.set_chunk_size(batch_size, batch_chunk_count)
 
         self.splits = splits
+
+        for dataset_name, split_loaders in splits.items():
+            count = sum([len(k) for k, _ in split_loaders])
+            logger.info(f"Dataset `{dataset_name}` contains {count} samples")
+
         self.cache_samples = cache_samples
         self._train_sampler = None
         self.balance_sampler_by_class = balance_sampler_by_class
diff --git a/src/ptbench/data/image_utils.py b/src/ptbench/data/image_utils.py
index b9f8c810..99df51c8 100644
--- a/src/ptbench/data/image_utils.py
+++ b/src/ptbench/data/image_utils.py
@@ -9,26 +9,6 @@ import numpy
 import PIL.Image
 
 
-class SingleAutoLevel16to8:
-    """Converts a 16-bit image to 8-bit representation using "auto-level".
-
-    This transform assumes that the input image is gray-scaled.
-
-    To auto-level, we calculate the maximum and the minimum of the
-    image, and consider such a range should be mapped to the [0,255]
-    range of the destination image.
-    """
-
-    def __call__(self, img):
-        imin, imax = img.getextrema()
-        irange = imax - imin
-        return PIL.Image.fromarray(
-            numpy.round(
-                255.0 * (numpy.array(img).astype(float) - imin) / irange
-            ).astype("uint8"),
-        ).convert("L")
-
-
 def remove_black_borders(
     img: PIL.Image.Image, threshold: int = 0
 ) -> PIL.Image.Image:
@@ -47,19 +27,23 @@ def remove_black_borders(
         A PIL image with black borders removed
     """
 
-    img = numpy.asarray(img)
+    img_array = numpy.asarray(img)
 
-    if len(img.shape) == 2:  # single channel
-        mask = numpy.asarray(img) > threshold
-        return PIL.Image.fromarray(img[numpy.ix_(mask.any(1), mask.any(0))])
+    if len(img_array.shape) == 2:  # single channel
+        mask = numpy.asarray(img_array) > threshold
+        return PIL.Image.fromarray(
+            img_array[numpy.ix_(mask.any(1), mask.any(0))]
+        )
 
-    elif len(img.shape) == 3 and img.shape[2] == 3:
-        r_mask = img[:, :, 0] > threshold
-        g_mask = img[:, :, 1] > threshold
-        b_mask = img[:, :, 2] > threshold
+    elif len(img_array.shape) == 3 and img_array.shape[2] == 3:
+        r_mask = img_array[:, :, 0] > threshold
+        g_mask = img_array[:, :, 1] > threshold
+        b_mask = img_array[:, :, 2] > threshold
 
         mask = r_mask | g_mask | b_mask
-        return PIL.Image.fromarray(img[numpy.ix_(mask.any(1), mask.any(0))])
+        return PIL.Image.fromarray(
+            img_array[numpy.ix_(mask.any(1), mask.any(0))]
+        )
 
     else:
         raise NotImplementedError
diff --git a/src/ptbench/data/nih_cxr14/__init__.py b/src/ptbench/data/nih_cxr14/__init__.py
index b9954cf1..e69de29b 100644
--- a/src/ptbench/data/nih_cxr14/__init__.py
+++ b/src/ptbench/data/nih_cxr14/__init__.py
@@ -1,97 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-"""NIH CXR14 (relabeled) dataset for computer-aided diagnosis.
-
-This dataset was extracted from the clinical PACS database at the National
-Institutes of Health Clinical Center (USA) and represents 60% of all
-their radiographs. It contains labels for fourteen common radiological
-signs in this order: cardiomegaly, emphysema, effusion, hernia, infiltration,
-mass, nodule, atelectasis, pneumothorax, pleural thickening, pneumonia,
-fibrosis, edema and consolidation.
-This is the relabeled version created in the CheXNeXt study.
-
-* Reference: [NIH-CXR14-2017]_
-* Original resolution (height x width or width x height): 1024 x 1024
-* Labels: [CHEXNEXT-2018]_
-* Split reference: [CHEXNEXT-2018]_
-* Protocol ``default``:
-
-  * Training samples: 98'637 (including labels)
-  * Validation samples: 6'350 (including labels)
-  * Test samples: 0
-
-* Protocol `ìdiap``:
-  * Images path adapted to Idiap infrastructure
-"""
-
-import importlib.resources
-import os
-
-from ...utils.rc import load_rc
-from ..dataset import JSONDataset
-from ..loader import load_pil_rgb, make_delayed
-
-_protocols = [
-    importlib.resources.files(__name__).joinpath("default.json.bz2"),
-    importlib.resources.files(__name__).joinpath("cardiomegaly.json.bz2"),
-]
-
-_datadir = load_rc().get("datadir.nih_cxr14_re", os.path.realpath(os.curdir))
-_idiap_folders = load_rc().get("nih_cxr14_re.idiap_folder_structure", False)
-
-
-def _raw_data_loader(sample):
-    if _idiap_folders:
-        # for folder lookup efficiency, data is split into subfolders
-        # each original file is on the subfolder `f[:5]/f`, where f
-        # is the original file basename
-        basename = os.path.basename(sample["data"])
-        return dict(
-            data=load_pil_rgb(
-                os.path.join(
-                    _datadir,
-                    os.path.dirname(sample["data"]),
-                    basename[:5],
-                    basename,
-                )
-            ),
-            label=sample["label"],
-        )
-    else:
-        return dict(
-            data=load_pil_rgb(os.path.join(_datadir, sample["data"])),
-            label=sample["label"],
-        )
-
-
-def _loader(context, sample):
-    # "context" is ignored in this case - database is homogeneous
-    # we returned delayed samples to avoid loading all images at once
-    return make_delayed(sample, _raw_data_loader)
-
-
-json_dataset = JSONDataset(
-    protocols=_protocols,
-    fieldnames=("data", "label"),
-    loader=_loader,
-)
-"""NIH CXR14 (relabeled) dataset object."""
-
-
-def _maker(protocol, size=512):
-    import torchvision.transforms as transforms
-
-    from .. import make_dataset
-
-    # ImageNet normalization
-    normalize = transforms.Normalize(
-        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-    )
-
-    return make_dataset(
-        [json_dataset.subsets(protocol)],
-        [transforms.Resize((size, size))],
-        [transforms.RandomHorizontalFlip()],
-        [transforms.ToTensor(), normalize],
-    )
diff --git a/src/ptbench/data/padchest/__init__.py b/src/ptbench/data/padchest/__init__.py
index 1151b55f..e69de29b 100644
--- a/src/ptbench/data/padchest/__init__.py
+++ b/src/ptbench/data/padchest/__init__.py
@@ -1,281 +0,0 @@
-# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
-#
-# SPDX-License-Identifier: GPL-3.0-or-later
-"""Padchest dataset for computer-aided diagnosis.
-
-A large chest x-ray image dataset with multi-label annotated reports.
-This dataset includes more than 160,000 images from 67,000 patients that were
-interpreted and reported by radiologists at Hospital San Juan (Spain) from 2009
-to 2017, covering six different position views and additional information on
-image acquisition and patient demography.
-
-We keep only "PA" images here.
-
-* Reference: [PADCHEST-2019]_
-* Original resolution: variable, original size
-* Labels: [PADCHEST-2019]_
-* Split reference: no split
-* Protocol ``default``:
-
-  * Training samples: 96'269
-  * Validation samples: 0
-  * Test samples: 0
-
-* Protocol `ìdiap``:
-  * Images path adapted to Idiap infrastructure
-
-* Labels:
-
-['COPD signs',
-'Chilaiditi sign',
-'NSG tube',
-'abnormal foreign body',
-'abscess',
-'adenopathy',
-'air bronchogram',
-'air fluid level',
-'air trapping',
-'alveolar pattern',
-'aortic aneurysm',
-'aortic atheromatosis',
-'aortic button enlargement',
-'aortic elongation',
-'aortic endoprosthesis',
-'apical pleural thickening',
-'artificial aortic heart valve',
-'artificial heart valve',
-'artificial mitral heart valve',
-'asbestosis signs',
-'ascendent aortic elongation',
-'atelectasis',
-'atelectasis basal',
-'atypical pneumonia',
-'axial hyperostosis',
-'azygoesophageal recess shift',
-'azygos lobe',
-'blastic bone lesion',
-'bone cement',
-'bone metastasis',
-'breast mass',
-'bronchiectasis',
-'bronchovascular markings',
-'bullas',
-'calcified adenopathy',
-'calcified densities',
-'calcified fibroadenoma',
-'calcified granuloma',
-'calcified mediastinal adenopathy',
-'calcified pleural plaques',
-'calcified pleural thickening',
-'callus rib fracture',
-'cardiomegaly',
-'catheter',
-'cavitation',
-'central vascular redistribution',
-'central venous catheter',
-'central venous catheter via jugular vein',
-'central venous catheter via subclavian vein',
-'central venous catheter via umbilical vein',
-'cervical rib',
-'chest drain tube',
-'chronic changes',
-'clavicle fracture',
-'consolidation',
-'costochondral junction hypertrophy',
-'costophrenic angle blunting',
-'cyst',
-'dai',
-'descendent aortic elongation',
-'dextrocardia',
-'diaphragmatic eventration',
-'double J stent',
-'dual chamber device',
-'electrical device',
-'emphysema',
-'empyema',
-'end on vessel',
-'endoprosthesis',
-'endotracheal tube',
-'esophagic dilatation',
-'exclude',
-'external foreign body',
-'fibrotic band',
-'fissure thickening',
-'flattened diaphragm',
-'fracture',
-'gastrostomy tube',
-'goiter',
-'granuloma',
-'ground glass pattern',
-'gynecomastia',
-'heart insufficiency',
-'heart valve calcified',
-'hemidiaphragm elevation',
-'hiatal hernia',
-'hilar congestion',
-'hilar enlargement',
-'humeral fracture',
-'humeral prosthesis',
-'hydropneumothorax',
-'hyperinflated lung',
-'hypoexpansion',
-'hypoexpansion basal',
-'increased density',
-'infiltrates',
-'interstitial pattern',
-'kerley lines',
-'kyphosis',
-'laminar atelectasis',
-'lepidic adenocarcinoma',
-'lipomatosis',
-'lobar atelectasis',
-'loculated fissural effusion',
-'loculated pleural effusion',
-'lung metastasis',
-'lung vascular paucity',
-'lymphangitis carcinomatosa',
-'lytic bone lesion',
-'major fissure thickening',
-'mammary prosthesis',
-'mass',
-'mastectomy',
-'mediastinal enlargement',
-'mediastinal mass',
-'mediastinal shift',
-'mediastinic lipomatosis',
-'metal',
-'miliary opacities',
-'minor fissure thickening',
-'multiple nodules',
-'nephrostomy tube',
-'nipple shadow',
-'nodule',
-'non axial articular degenerative changes',
-'normal',
-'obesity',
-'osteopenia',
-'osteoporosis',
-'osteosynthesis material',
-'pacemaker',
-'pectum carinatum',
-'pectum excavatum',
-'pericardial effusion',
-'pleural effusion',
-'pleural mass',
-'pleural plaques',
-'pleural thickening',
-'pneumomediastinum',
-'pneumonia',
-'pneumoperitoneo',
-'pneumothorax',
-'post radiotherapy changes',
-'prosthesis',
-'pseudonodule',
-'pulmonary artery enlargement',
-'pulmonary artery hypertension',
-'pulmonary edema',
-'pulmonary fibrosis',
-'pulmonary hypertension',
-'pulmonary mass',
-'pulmonary venous hypertension',
-'reservoir central venous catheter',
-'respiratory distress',
-'reticular interstitial pattern',
-'reticulonodular interstitial pattern',
-'rib fracture',
-'right sided aortic arch',
-'round atelectasis',
-'sclerotic bone lesion',
-'scoliosis',
-'segmental atelectasis',
-'single chamber device',
-'soft tissue mass',
-'sternoclavicular junction hypertrophy',
-'sternotomy',
-'subacromial space narrowing',
-'subcutaneous emphysema',
-'suboptimal study',
-'superior mediastinal enlargement',
-'supra aortic elongation',
-'surgery',
-'surgery breast',
-'surgery heart',
-'surgery humeral',
-'surgery lung',
-'surgery neck',
-'suture material',
-'thoracic cage deformation',
-'total atelectasis',
-'tracheal shift',
-'tracheostomy tube',
-'tuberculosis',
-'tuberculosis sequelae',
-'unchanged',
-'vascular hilar enlargement',
-'vascular redistribution',
-'ventriculoperitoneal drain tube',
-'vertebral anterior compression',
-'vertebral compression',
-'vertebral degenerative changes',
-'vertebral fracture',
-'volume loss']
-"""
-
-import importlib.resources
-import os
-
-from ...utils.rc import load_rc
-from .. import make_dataset
-from ..dataset import JSONDataset
-from ..loader import load_pil, make_delayed
-
-_protocols = [
-    importlib.resources.files(__name__).joinpath("idiap.json.bz2"),
-    importlib.resources.files(__name__).joinpath("tb_idiap.json.bz2"),
-    importlib.resources.files(__name__).joinpath("no_tb_idiap.json.bz2"),
-    importlib.resources.files(__name__).joinpath("cardiomegaly_idiap.json.bz2"),
-]
-
-_datadir = load_rc().get("datadir.padchest", os.path.realpath(os.curdir))
-
-
-def _raw_data_loader(sample):
-    return dict(
-        data=load_pil(os.path.join(_datadir, sample["data"])),
-        label=sample["label"],
-    )
-
-
-def _loader(context, sample):
-    # "context" is ignored in this case - database is homogeneous
-    # we returned delayed samples to avoid loading all images at once
-    return make_delayed(sample, _raw_data_loader)
-
-
-json_dataset = JSONDataset(
-    protocols=_protocols,
-    fieldnames=("data", "label"),
-    loader=_loader,
-)
-"""Padchest dataset object."""
-
-
-def _maker(protocol, resize_size=512, cc_size=512, RGB=True):
-    import torchvision.transforms as transforms
-
-    from ..loader import SingleAutoLevel16to8
-
-    post_transforms = []
-    if not RGB:
-        post_transforms = [transforms.Lambda(lambda x: x.convert("L"))]
-
-    return make_dataset(
-        [json_dataset.subsets(protocol)],
-        [
-            SingleAutoLevel16to8(),
-            transforms.Resize(resize_size),
-            transforms.CenterCrop(cc_size),
-        ],
-        [transforms.RandomHorizontalFlip()],
-        post_transforms,
-    )
diff --git a/src/ptbench/data/padchest/cardiomegaly_idiap.json b/src/ptbench/data/padchest/cardiomegaly_idiap.json
new file mode 100644
index 00000000..9487ba83
--- /dev/null
+++ b/src/ptbench/data/padchest/cardiomegaly_idiap.json
@@ -0,0 +1,164 @@
+{
+  "train": [
+    [
+      "0/128060267991587422580475423001969971705_pjiogk.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/62251368659414281768550499840080640229_8jw2hb.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/30661727075761817007267292459310975718_86nsuj.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/84545123491560549832532130982226930123_vvayv1.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/284353054075043225622260270287627142906_kduagg.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/172366760559200399420370251768770096562_31ewgy.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/87180314458623865269977662447356279507_tcw56c.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/229647061532869405127486256298724729451_o2d0lv.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/110057948387370363009458390264986734403-3_pbni4p.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/110057948387370363009458390264986734403_myst7q.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/13028631954019256091081365260375513084_9b0d5l.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/304673909503207424801802419702123736315_vyckk1.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/163750344543947776171909966951769207897_dgf4vk.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/159216970546428043543933950339353602394_t0rgza.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/220991425902150564296792633654689602163_hrvmt4.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/227808586259025315397189404192472871023_2_ba4iu6.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/289108620230468119174968226927683707287_9wupg2.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/159364525876157332485573893353941089455_aepmh7.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/262822530217543635254713021579502738735_g2v51e.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/172167226340244841253091101374217956877_q2xlp3.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
+      "0/1256842362861431725328351539259305635_u1qifz.png",
+      [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    ],
+    [
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+}
diff --git a/src/ptbench/data/padchest/cardiomegaly_idiap.json.bz2 b/src/ptbench/data/padchest/cardiomegaly_idiap.json.bz2
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diff --git a/src/ptbench/data/padchest/cardiomegaly_idiap.py b/src/ptbench/data/padchest/cardiomegaly_idiap.py
index d2538738..05286fd5 100644
--- a/src/ptbench/data/padchest/cardiomegaly_idiap.py
+++ b/src/ptbench/data/padchest/cardiomegaly_idiap.py
@@ -1,48 +1,14 @@
 # SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
-"""Padchest cardiomegaly (idiap protocol) dataset for computer-aided diagnosis.
-
-The first 40 images with cardiomegaly.
-parameters: Label = "Normal", MethodLabel = "Physician", Projection = "PA"
-
-* Split reference: first 100% of cardiomegaly for "train"
-* See :py:mod:`ptbench.data.padchest` for dataset details
-* This configuration resolution: 512 x 512 (default)
-"""
-
-from clapper.logging import setup
 
-from .. import return_subsets
-from ..base_datamodule import BaseDataModule
-from . import _maker
+from .datamodule import 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,
-    ):
-        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):
-        self.dataset = _maker("cardiomegaly_idiap", RGB=False)
-        (
-            self.train_dataset,
-            self.validation_dataset,
-            self.extra_validation_datasets,
-            self.predict_dataset,
-        ) = return_subsets(self.dataset)
+datamodule = DataModule("cardiomegaly_idiap.json")
+"""Padchest cardiomegaly (idiap protocol) dataset for computer-aided diagnosis.
 
+This split contains the first 40 images with cardiomegaly, with parameters:
+Label = "Normal", MethodLabel = "Physician", Projection = "PA"
 
-datamodule = DefaultModule
+Read documentation of :py:class:`DataModule` for technical details.
+"""
diff --git a/src/ptbench/data/padchest/datamodule.py b/src/ptbench/data/padchest/datamodule.py
new file mode 100644
index 00000000..5db73888
--- /dev/null
+++ b/src/ptbench/data/padchest/datamodule.py
@@ -0,0 +1,333 @@
+# SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
+#
+# SPDX-License-Identifier: GPL-3.0-or-later
+
+import importlib.resources
+import os
+
+import numpy
+import PIL.Image
+
+from torchvision.transforms.functional import center_crop, to_tensor
+
+from ...utils.rc import load_rc
+from ..datamodule import CachingDataModule
+from ..image_utils import remove_black_borders
+from ..split import JSONDatabaseSplit
+from ..typing import DatabaseSplit
+from ..typing import RawDataLoader as _BaseRawDataLoader
+from ..typing import Sample
+
+
+class RawDataLoader(_BaseRawDataLoader):
+    """A specialized raw-data-loader for the PadChest dataset.
+
+    Attributes
+    ----------
+
+    datadir
+        This variable contains the base directory where the database raw data
+        is stored.
+    """
+
+    datadir: str
+
+    def __init__(self):
+        rc = load_rc()
+        self.datadir = rc.get("datadir.padchest", os.path.realpath(os.curdir))
+
+    def sample(self, sample: tuple[str, list[int]]) -> Sample:
+        """Loads a single image sample from the disk.
+
+        Parameters
+        ----------
+
+        sample:
+            A tuple containing the path suffix, within the dataset root folder,
+            where to find the image to be loaded, and an integer, representing the
+            sample label.
+
+
+        Returns
+        -------
+
+        sample
+            The sample representation
+        """
+        # N.B.: PadChest images are encoded as 16-bit grayscale images
+        image = PIL.Image.open(os.path.join(self.datadir, sample[0]))
+        image = remove_black_borders(image)
+        array = numpy.array(image).astype(numpy.float32) / 65536
+        tensor = to_tensor(array)
+        tensor = center_crop(tensor, min(*tensor.shape[1:]))
+
+        # use the code below to view generated images
+        # from torchvision.transforms.functional import to_pil_image
+        # to_pil_image(tensor).show()
+        # __import__("pdb").set_trace()
+
+        return tensor, dict(label=sample[1], name=sample[0])  # type: ignore[arg-type]
+
+    def label(self, sample: tuple[str, list[int]]) -> list[int]:
+        """Loads a single image sample label from the disk.
+
+        Parameters
+        ----------
+
+        sample:
+            A tuple containing the path suffix, within the dataset root folder,
+            where to find the image to be loaded, and an integer, representing the
+            sample label.
+
+
+        Returns
+        -------
+
+        labels
+            The integer labels associated with the sample
+        """
+        return sample[1]
+
+
+def make_split(basename: str) -> DatabaseSplit:
+    """Returns a database split for the NIH CXR-14 database."""
+
+    return JSONDatabaseSplit(
+        importlib.resources.files(__name__.rsplit(".", 1)[0]).joinpath(basename)
+    )
+
+
+class DataModule(CachingDataModule):
+    """Padchest dataset for computer-aided diagnosis.
+
+    A large chest x-ray image dataset with multi-label annotated reports. This
+    dataset includes more than 160,000 images from 67,000 patients that were
+    interpreted and reported by radiologists at Hospital San Juan (Spain) from
+    2009 to 2017, covering six different position views and additional
+    information on image acquisition and patient demography.
+
+    We keep only postero-anterior "PA" images in our setup.
+
+    * Reference: [PADCHEST-2019]_
+    * Raw data input (on disk):
+
+      * PNG grayscale 16-bit depth images
+      * Resolution: varying resolution
+
+    * Labels: [PADCHEST-2019]_
+    * Output image:
+
+        * Transforms:
+
+            * Load raw 16-bit PNG with :py:mod:`PIL`
+            * Remove excess black borders
+            * Convert image to 32-bit floats between 0. and 1.
+            * Convert to tensor
+            * Center crop, preserving the minimum dimension (height or width)
+
+        * Final specifications
+
+            * Grayscale, encoded as a 1-plane 32-bit float image, square with
+              varying resolutions depending on the raw input image
+            * Labels, in order (some of which may not be present in all splits):
+
+              * COPD signs
+              * Chilaiditi sign
+              * NSG tube
+              * abnormal foreign body
+              * abscess
+              * adenopathy
+              * air bronchogram
+              * air fluid level
+              * air trapping
+              * alveolar pattern
+              * aortic aneurysm
+              * aortic atheromatosis
+              * aortic button enlargement
+              * aortic elongation
+              * aortic endoprosthesis
+              * apical pleural thickening
+              * artificial aortic heart valve
+              * artificial heart valve
+              * artificial mitral heart valve
+              * asbestosis signs
+              * ascendent aortic elongation
+              * atelectasis
+              * atelectasis basal
+              * atypical pneumonia
+              * axial hyperostosis
+              * azygoesophageal recess shift
+              * azygos lobe
+              * blastic bone lesion
+              * bone cement
+              * bone metastasis
+              * breast mass
+              * bronchiectasis
+              * bronchovascular markings
+              * bullas
+              * calcified adenopathy
+              * calcified densities
+              * calcified fibroadenoma
+              * calcified granuloma
+              * calcified mediastinal adenopathy
+              * calcified pleural plaques
+              * calcified pleural thickening
+              * callus rib fracture
+              * cardiomegaly
+              * catheter
+              * cavitation
+              * central vascular redistribution
+              * central venous catheter
+              * central venous catheter via jugular vein
+              * central venous catheter via subclavian vein
+              * central venous catheter via umbilical vein
+              * cervical rib
+              * chest drain tube
+              * chronic changes
+              * clavicle fracture
+              * consolidation
+              * costochondral junction hypertrophy
+              * costophrenic angle blunting
+              * cyst
+              * dai
+              * descendent aortic elongation
+              * dextrocardia
+              * diaphragmatic eventration
+              * double J stent
+              * dual chamber device
+              * electrical device
+              * emphysema
+              * empyema
+              * end on vessel
+              * endoprosthesis
+              * endotracheal tube
+              * esophagic dilatation
+              * exclude
+              * external foreign body
+              * fibrotic band
+              * fissure thickening
+              * flattened diaphragm
+              * fracture
+              * gastrostomy tube
+              * goiter
+              * granuloma
+              * ground glass pattern
+              * gynecomastia
+              * heart insufficiency
+              * heart valve calcified
+              * hemidiaphragm elevation
+              * hiatal hernia
+              * hilar congestion
+              * hilar enlargement
+              * humeral fracture
+              * humeral prosthesis
+              * hydropneumothorax
+              * hyperinflated lung
+              * hypoexpansion
+              * hypoexpansion basal
+              * increased density
+              * infiltrates
+              * interstitial pattern
+              * kerley lines
+              * kyphosis
+              * laminar atelectasis
+              * lepidic adenocarcinoma
+              * lipomatosis
+              * lobar atelectasis
+              * loculated fissural effusion
+              * loculated pleural effusion
+              * lung metastasis
+              * lung vascular paucity
+              * lymphangitis carcinomatosa
+              * lytic bone lesion
+              * major fissure thickening
+              * mammary prosthesis
+              * mass
+              * mastectomy
+              * mediastinal enlargement
+              * mediastinal mass
+              * mediastinal shift
+              * mediastinic lipomatosis
+              * metal
+              * miliary opacities
+              * minor fissure thickening
+              * multiple nodules
+              * nephrostomy tube
+              * nipple shadow
+              * nodule
+              * non axial articular degenerative changes
+              * normal
+              * obesity
+              * osteopenia
+              * osteoporosis
+              * osteosynthesis material
+              * pacemaker
+              * pectum carinatum
+              * pectum excavatum
+              * pericardial effusion
+              * pleural effusion
+              * pleural mass
+              * pleural plaques
+              * pleural thickening
+              * pneumomediastinum
+              * pneumonia
+              * pneumoperitoneo
+              * pneumothorax
+              * post radiotherapy changes
+              * prosthesis
+              * pseudonodule
+              * pulmonary artery enlargement
+              * pulmonary artery hypertension
+              * pulmonary edema
+              * pulmonary fibrosis
+              * pulmonary hypertension
+              * pulmonary mass
+              * pulmonary venous hypertension
+              * reservoir central venous catheter
+              * respiratory distress
+              * reticular interstitial pattern
+              * reticulonodular interstitial pattern
+              * rib fracture
+              * right sided aortic arch
+              * round atelectasis
+              * sclerotic bone lesion
+              * scoliosis
+              * segmental atelectasis
+              * single chamber device
+              * soft tissue mass
+              * sternoclavicular junction hypertrophy
+              * sternotomy
+              * subacromial space narrowing
+              * subcutaneous emphysema
+              * suboptimal study
+              * superior mediastinal enlargement
+              * supra aortic elongation
+              * surgery
+              * surgery breast
+              * surgery heart
+              * surgery humeral
+              * surgery lung
+              * surgery neck
+              * suture material
+              * thoracic cage deformation
+              * total atelectasis
+              * tracheal shift
+              * tracheostomy tube
+              * tuberculosis
+              * tuberculosis sequelae
+              * unchanged
+              * vascular hilar enlargement
+              * vascular redistribution
+              * ventriculoperitoneal drain tube
+              * vertebral anterior compression
+              * vertebral compression
+              * vertebral degenerative changes
+              * vertebral fracture
+              * volume loss
+    """
+
+    def __init__(self, split_filename: str):
+        super().__init__(
+            database_split=make_split(split_filename),
+            raw_data_loader=RawDataLoader(),
+        )
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diff --git a/src/ptbench/data/padchest/idiap.py b/src/ptbench/data/padchest/idiap.py
index 284a944d..ea8b3dbd 100644
--- a/src/ptbench/data/padchest/idiap.py
+++ b/src/ptbench/data/padchest/idiap.py
@@ -1,44 +1,17 @@
 # SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
-"""Padchest (idiap protocol) dataset for computer-aided diagnosis.
 
-* See :py:mod:`ptbench.data.padchest` for dataset details
-* This configuration resolution: 512 x 512 (default)
-"""
-
-from clapper.logging import setup
-
-from .. import return_subsets
-from ..base_datamodule import BaseDataModule
-from . import _maker
-
-logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+from .datamodule import DataModule
 
+datamodule = DataModule("idiap.json.bz2")
+"""Padchest dataset for computer-aided diagnosis (``idiap`` split).
 
-class DefaultModule(BaseDataModule):
-    def __init__(
-        self,
-        train_batch_size=1,
-        predict_batch_size=1,
-        drop_incomplete_batch=False,
-        multiproc_kwargs=None,
-    ):
-        super().__init__(
-            train_batch_size=train_batch_size,
-            predict_batch_size=predict_batch_size,
-            drop_incomplete_batch=drop_incomplete_batch,
-            multiproc_kwargs=multiproc_kwargs,
-        )
+This split contains all images in the database.  Read documentation of
+:py:class:`DataModule` for technical details.
 
-    def setup(self, stage: str):
-        self.dataset = _maker("idiap")
-        (
-            self.train_dataset,
-            self.validation_dataset,
-            self.extra_validation_datasets,
-            self.predict_dataset,
-        ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
+* Split reference: ours
+  * Training samples: 96'269
+  * Validation samples: ?
+  * Test samples: ?
+"""
diff --git a/src/ptbench/data/padchest/no-tb-idiap.json.bz2 b/src/ptbench/data/padchest/no-tb-idiap.json.bz2
new file mode 100644
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diff --git a/src/ptbench/data/padchest/no_tb_idiap.json.bz2 b/src/ptbench/data/padchest/no_tb_idiap.json.bz2
deleted file mode 100644
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diff --git a/src/ptbench/data/padchest/no_tb_idiap.py b/src/ptbench/data/padchest/no_tb_idiap.py
index a424e0fe..62e4b56b 100644
--- a/src/ptbench/data/padchest/no_tb_idiap.py
+++ b/src/ptbench/data/padchest/no_tb_idiap.py
@@ -1,10 +1,11 @@
 # SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
-"""Padchest tuberculosis (no TB idiap protocol) dataset for computer-aided
-diagnosis.
 
-* Protocol ``no TB idiap``:
+from .datamodule import DataModule
+
+datamodule = DataModule("no-tb-idiap.json.bz2")
+"""Padchest dataset for computer-aided diagnosis (``no-tb-idiap`` split).
 
   * Training samples: 20'126
   * Validation samples: 1'500
@@ -12,44 +13,21 @@ diagnosis.
 
 * Images path adapted to Idiap infrastructure
 
-* Labels:
-  cardiomegaly, emphysema, effusion, hernia, infiltration,
-  mass, nodule, atelectasis, pneumothorax, pleural thickening, pneumonia,
-  fibrosis, edema and consolidation
+* Labels (in order):
+
+  * cardiomegaly
+  * emphysema
+  * effusion
+  * hernia
+  * infiltration
+  * mass
+  * nodule
+  * atelectasis
+  * pneumothorax
+  * pleural thickening
+  * pneumonia
+  * fibrosis
+  * edema and consolidation
+
+Read documentation of :py:class:`DataModule` for technical details.
 """
-
-from clapper.logging import setup
-
-from .. import return_subsets
-from ..base_datamodule import BaseDataModule
-from . import _maker
-
-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,
-    ):
-        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):
-        self.dataset = _maker("no_tb_idiap")
-        (
-            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/padchest/tb-idiap.json b/src/ptbench/data/padchest/tb-idiap.json
new file mode 100644
index 00000000..cfa02579
--- /dev/null
+++ b/src/ptbench/data/padchest/tb-idiap.json
@@ -0,0 +1,256 @@
+{
+  "train": [
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diff --git a/src/ptbench/data/padchest/tb_idiap.py b/src/ptbench/data/padchest/tb_idiap.py
index 1a251620..69c2708b 100644
--- a/src/ptbench/data/padchest/tb_idiap.py
+++ b/src/ptbench/data/padchest/tb_idiap.py
@@ -1,48 +1,15 @@
 # SPDX-FileCopyrightText: Copyright © 2023 Idiap Research Institute <contact@idiap.ch>
 #
 # SPDX-License-Identifier: GPL-3.0-or-later
-"""Padchest tuberculosis (idiap protocol) dataset for computer-aided diagnosis.
 
-The 125 healthy images are the first 125 padchest images with the following
-parameters: Label = "Normal", MethodLabel = "Physician", Projection = "PA"
+from .datamodule import DataModule
 
-* Split reference: first 80% of TB and healthy CXR for "train", rest for "test"
-* See :py:mod:`ptbench.data.padchest` for dataset details
-* This configuration resolution: 512 x 512 (default)
-"""
-
-from clapper.logging import setup
-
-from .. import return_subsets
-from ..base_datamodule import BaseDataModule
-from . import _maker
-
-logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+datamodule = DataModule("tb-idiap.json")
+"""Padchest dataset for computer-aided diagnosis (``tb-idiap`` split).
 
+This split contains 125 healthy images are the first 125 padchest images with
+the following parameters: Label = "Normal", MethodLabel = "Physician",
+Projection = "PA"
 
-class DefaultModule(BaseDataModule):
-    def __init__(
-        self,
-        train_batch_size=1,
-        predict_batch_size=1,
-        drop_incomplete_batch=False,
-        multiproc_kwargs=None,
-    ):
-        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):
-        self.dataset = _maker("tb_idiap", RGB=False)
-        (
-            self.train_dataset,
-            self.validation_dataset,
-            self.extra_validation_datasets,
-            self.predict_dataset,
-        ) = return_subsets(self.dataset)
-
-
-datamodule = DefaultModule
+Read documentation of :py:class:`DataModule` for technical details.
+"""
-- 
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