diff --git a/src/ptbench/data/__init__.py b/src/ptbench/data/__init__.py
index 2617ec0f2a5dfc7e294035a8debf153cfe83062b..516af66bcae176129d985555b6b7facd885ce5b4 100644
--- a/src/ptbench/data/__init__.py
+++ b/src/ptbench/data/__init__.py
@@ -1 +1,345 @@
"""Data manipulation and raw dataset definitions."""
+
+import random
+
+import torch
+
+from clapper.logging import setup
+
+logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+
+
+def make_subset(samples, transforms=[], prefixes=[], suffixes=[]):
+ """Creates a new data set, applying transforms.
+
+ .. note::
+
+ This is a convenience function for our own dataset definitions inside
+ this module, guaranteeting homogenity between dataset definitions
+ provided in this package. It assumes certain strategies for data
+ augmentation that may not be translatable to other applications.
+
+
+ Parameters
+ ----------
+
+ samples : list
+ List of delayed samples
+
+ transforms : list
+ A list of transforms that needs to be applied to all samples in the set
+
+ prefixes : list
+ A list of data augmentation operations that needs to be applied
+ **before** the transforms above
+
+ suffixes : list
+ A list of data augmentation operations that needs to be applied
+ **after** the transforms above
+
+
+ Returns
+ -------
+
+ subset : :py:class:`ptbench.data.utils.SampleListDataset`
+ A pre-formatted dataset that can be fed to one of our engines
+ """
+ from .utils import SampleListDataset as wrapper
+
+ return wrapper(samples, prefixes + transforms + suffixes)
+
+
+def make_dataset(
+ subsets_groups, transforms=[], t_transforms=[], post_transforms=[]
+):
+ """Creates a new configuration dataset from a list of dictionaries and
+ transforms.
+
+ This function takes as input a list of dictionaries as those that can be
+ returned by :py:meth:`ptbench.data.dataset.JSONDataset.subsets`
+ mapping protocol names (such as ``train``, ``dev`` and ``test``) to
+ :py:class:`ptbench.data.sample.DelayedSample` lists, and a set of
+ transforms, and returns a dictionary applying
+ :py:class:`ptbench.data.utils.SampleListDataset` to these
+ lists, and our standard data augmentation if a ``train`` set exists.
+
+ For example, if ``subsets`` is composed of two sets named ``train`` and
+ ``test``, this function will yield a dictionary with the following entries:
+
+ * ``__train__``: Wraps the ``train`` subset, includes data augmentation
+ (note: datasets with names starting with ``_`` (underscore) are excluded
+ from prediction and evaluation by default, as they contain data
+ augmentation transformations.)
+ * ``train``: Wraps the ``train`` subset, **without** data augmentation
+ * ``test``: Wraps the ``test`` subset, **without** data augmentation
+
+ .. note::
+
+ This is a convenience function for our own dataset definitions inside
+ this module, guaranteeting homogenity between dataset definitions
+ provided in this package. It assumes certain strategies for data
+ augmentation that may not be translatable to other applications.
+
+
+ Parameters
+ ----------
+
+ subsets : list
+ A list of dictionaries that contains the delayed sample lists
+ for a number of named lists. The subsets will be aggregated in one
+ final subset. If one of the keys is ``train``, our standard dataset
+ augmentation transforms are appended to the definition of that subset.
+ All other subsets remain un-augmented.
+
+ transforms : list
+ A list of transforms that needs to be applied to all samples in the set
+
+ t_transforms : list
+ A list of transforms that needs to be applied to the train samples
+
+ post_transforms : list
+ A list of transforms that needs to be applied to all samples in the set
+ after all the other transforms
+
+
+ Returns
+ -------
+
+ dataset : dict
+ A pre-formatted dataset that can be fed to one of our engines. It maps
+ string names to :py:class:`ptbench.data.utils.SampleListDataset`'s.
+ """
+
+ retval = {}
+
+ if len(subsets_groups) == 1:
+ subsets = subsets_groups[0]
+ else:
+ # If multiple subsets groups: aggregation
+ aggregated_subsets = {}
+ for subsets in subsets_groups:
+ for key in subsets.keys():
+ if key in aggregated_subsets:
+ aggregated_subsets[key] += subsets[key]
+ # Shuffle if data comes from multiple datasets
+ random.shuffle(aggregated_subsets[key])
+ else:
+ aggregated_subsets[key] = subsets[key]
+ subsets = aggregated_subsets
+
+ # Add post_transforms after t_transforms for the train set
+ t_transforms += post_transforms
+
+ for key in subsets.keys():
+ retval[key] = make_subset(
+ subsets[key], transforms=transforms, suffixes=post_transforms
+ )
+ if key == "train":
+ retval["__train__"] = make_subset(
+ subsets[key], transforms=transforms, suffixes=(t_transforms)
+ )
+ if key == "validation":
+ # also use it for validation during training
+ retval["__valid__"] = retval[key]
+
+ if (
+ ("__train__" in retval)
+ and ("train" in retval)
+ and ("__valid__" not in retval)
+ ):
+ # if the dataset does not have a validation set, we use the unaugmented
+ # training set as validation set
+ retval["__valid__"] = retval["train"]
+
+ return retval
+
+
+def get_samples_weights(dataset):
+ """Compute the weights of all the samples of the dataset to balance it
+ using the sampler of the dataloader.
+
+ This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`
+ and computes the weights to balance each class in the dataset and the
+ datasets themselves if we have a ConcatDataset.
+
+
+ Parameters
+ ----------
+
+ dataset : torch.utils.data.dataset.Dataset
+ An instance of torch.utils.data.dataset.Dataset
+ ConcatDataset are supported
+
+
+ Returns
+ -------
+
+ samples_weights : :py:class:`torch.Tensor`
+ the weights for all the samples in the dataset given as input
+ """
+ samples_weights = []
+
+ if isinstance(dataset, torch.utils.data.ConcatDataset):
+ for ds in dataset.datasets:
+ # Weighting only for binary labels
+ if isinstance(ds._samples[0].label, int):
+ # Groundtruth
+ targets = []
+ for s in ds._samples:
+ targets.append(s.label)
+ targets = torch.tensor(targets)
+
+ # Count number of samples per class
+ class_sample_count = torch.tensor(
+ [
+ (targets == t).sum()
+ for t in torch.unique(targets, sorted=True)
+ ]
+ )
+
+ weight = 1.0 / class_sample_count.float()
+
+ samples_weights.append(
+ torch.tensor([weight[t] for t in targets])
+ )
+
+ else:
+ # We only weight to sample equally from each dataset
+ samples_weights.append(torch.full((len(ds),), 1.0 / len(ds)))
+
+ # Concatenate sample weights from all the datasets
+ samples_weights = torch.cat(samples_weights)
+
+ else:
+ # Weighting only for binary labels
+ if isinstance(dataset._samples[0].label, int):
+ # Groundtruth
+ targets = []
+ for s in dataset._samples:
+ targets.append(s.label)
+ targets = torch.tensor(targets)
+
+ # Count number of samples per class
+ class_sample_count = torch.tensor(
+ [
+ (targets == t).sum()
+ for t in torch.unique(targets, sorted=True)
+ ]
+ )
+
+ weight = 1.0 / class_sample_count.float()
+
+ samples_weights = torch.tensor([weight[t] for t in targets])
+
+ else:
+ # Equal weights for non-binary labels
+ samples_weights = torch.ones(len(dataset._samples))
+
+ return samples_weights
+
+
+def get_positive_weights(dataset):
+ """Compute the positive weights of each class of the dataset to balance the
+ BCEWithLogitsLoss criterion.
+
+ This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`
+ and computes the positive weights of each class to use them to have
+ a balanced loss.
+
+
+ Parameters
+ ----------
+
+ dataset : torch.utils.data.dataset.Dataset
+ An instance of torch.utils.data.dataset.Dataset
+ ConcatDataset are supported
+
+
+ Returns
+ -------
+
+ positive_weights : :py:class:`torch.Tensor`
+ the positive weight of each class in the dataset given as input
+ """
+ targets = []
+
+ if isinstance(dataset, torch.utils.data.ConcatDataset):
+ for ds in dataset.datasets:
+ for s in ds._samples:
+ targets.append(s.label)
+
+ else:
+ for s in dataset._samples:
+ targets.append(s.label)
+
+ targets = torch.tensor(targets)
+
+ # Binary labels
+ if len(list(targets.shape)) == 1:
+ class_sample_count = [
+ float((targets == t).sum().item())
+ for t in torch.unique(targets, sorted=True)
+ ]
+
+ # Divide negatives by positives
+ positive_weights = torch.tensor(
+ [class_sample_count[0] / class_sample_count[1]]
+ ).reshape(-1)
+
+ # Multiclass labels
+ else:
+ class_sample_count = torch.sum(targets, dim=0)
+ negative_class_sample_count = (
+ torch.full((targets.size()[1],), float(targets.size()[0]))
+ - class_sample_count
+ )
+
+ positive_weights = negative_class_sample_count / (
+ class_sample_count + negative_class_sample_count
+ )
+
+ return positive_weights
+
+
+def return_subsets(dataset):
+ train_dataset = None
+ validation_dataset = None
+ extra_validation_datasets = None
+ predict_dataset = None
+
+ if "__train__" in dataset:
+ logger.info("Found (dedicated) '__train__' set for training")
+ train_dataset = dataset["__train__"]
+ else:
+ train_dataset = dataset["train"]
+
+ if "__valid__" in dataset:
+ logger.info("Found (dedicated) '__valid__' set for validation")
+ validation_dataset = dataset["__valid__"]
+
+ if "__extra_valid__" in dataset:
+ if not isinstance(dataset["__extra_valid__"], list):
+ raise RuntimeError(
+ f"If present, dataset['__extra_valid__'] must be a list, "
+ f"but you passed a {type(dataset['__extra_valid__'])}, "
+ f"which is invalid."
+ )
+ logger.info(
+ f"Found {len(dataset['__extra_valid__'])} extra validation "
+ f"set(s) to be tracked during training"
+ )
+ logger.info(
+ "Extra validation sets are NOT used for model checkpointing!"
+ )
+ extra_validation_datasets = dataset["__extra_valid__"]
+ else:
+ extra_validation_datasets = None
+
+ predict_dataset = dataset
+
+ return (
+ train_dataset,
+ validation_dataset,
+ extra_validation_datasets,
+ predict_dataset,
+ )
diff --git a/src/ptbench/data/shenzhen/__init__.py b/src/ptbench/data/shenzhen/__init__.py
index 60a004a3afc76239ffae144556feaaa888850ab7..a854e559bc033cd12eff0b10fc246b47edd73b64 100644
--- a/src/ptbench/data/shenzhen/__init__.py
+++ b/src/ptbench/data/shenzhen/__init__.py
@@ -22,13 +22,11 @@ the daily routine using Philips DR Digital Diagnose systems.
import importlib.resources
import os
-import random
-
-import torch
from clapper.logging import setup
from ...utils.rc import load_rc
+from .. import make_dataset
from ..dataset import JSONDataset
from ..loader import load_pil_baw, make_delayed
@@ -93,339 +91,3 @@ def _maker(protocol, resize_size=512, cc_size=512, RGB=False):
[ElasticDeformation(p=0.8)],
post_transforms,
)
-
-
-def make_subset(samples, transforms=[], prefixes=[], suffixes=[]):
- """Creates a new data set, applying transforms.
-
- .. note::
-
- This is a convenience function for our own dataset definitions inside
- this module, guaranteeting homogenity between dataset definitions
- provided in this package. It assumes certain strategies for data
- augmentation that may not be translatable to other applications.
-
-
- Parameters
- ----------
-
- samples : list
- List of delayed samples
-
- transforms : list
- A list of transforms that needs to be applied to all samples in the set
-
- prefixes : list
- A list of data augmentation operations that needs to be applied
- **before** the transforms above
-
- suffixes : list
- A list of data augmentation operations that needs to be applied
- **after** the transforms above
-
-
- Returns
- -------
-
- subset : :py:class:`ptbench.data.utils.SampleListDataset`
- A pre-formatted dataset that can be fed to one of our engines
- """
- from ...data.utils import SampleListDataset as wrapper
-
- return wrapper(samples, prefixes + transforms + suffixes)
-
-
-def make_dataset(
- subsets_groups, transforms=[], t_transforms=[], post_transforms=[]
-):
- """Creates a new configuration dataset from a list of dictionaries and
- transforms.
-
- This function takes as input a list of dictionaries as those that can be
- returned by :py:meth:`ptbench.data.dataset.JSONDataset.subsets`
- mapping protocol names (such as ``train``, ``dev`` and ``test``) to
- :py:class:`ptbench.data.sample.DelayedSample` lists, and a set of
- transforms, and returns a dictionary applying
- :py:class:`ptbench.data.utils.SampleListDataset` to these
- lists, and our standard data augmentation if a ``train`` set exists.
-
- For example, if ``subsets`` is composed of two sets named ``train`` and
- ``test``, this function will yield a dictionary with the following entries:
-
- * ``__train__``: Wraps the ``train`` subset, includes data augmentation
- (note: datasets with names starting with ``_`` (underscore) are excluded
- from prediction and evaluation by default, as they contain data
- augmentation transformations.)
- * ``train``: Wraps the ``train`` subset, **without** data augmentation
- * ``test``: Wraps the ``test`` subset, **without** data augmentation
-
- .. note::
-
- This is a convenience function for our own dataset definitions inside
- this module, guaranteeting homogenity between dataset definitions
- provided in this package. It assumes certain strategies for data
- augmentation that may not be translatable to other applications.
-
-
- Parameters
- ----------
-
- subsets : list
- A list of dictionaries that contains the delayed sample lists
- for a number of named lists. The subsets will be aggregated in one
- final subset. If one of the keys is ``train``, our standard dataset
- augmentation transforms are appended to the definition of that subset.
- All other subsets remain un-augmented.
-
- transforms : list
- A list of transforms that needs to be applied to all samples in the set
-
- t_transforms : list
- A list of transforms that needs to be applied to the train samples
-
- post_transforms : list
- A list of transforms that needs to be applied to all samples in the set
- after all the other transforms
-
-
- Returns
- -------
-
- dataset : dict
- A pre-formatted dataset that can be fed to one of our engines. It maps
- string names to :py:class:`ptbench.data.utils.SampleListDataset`'s.
- """
-
- retval = {}
-
- if len(subsets_groups) == 1:
- subsets = subsets_groups[0]
- else:
- # If multiple subsets groups: aggregation
- aggregated_subsets = {}
- for subsets in subsets_groups:
- for key in subsets.keys():
- if key in aggregated_subsets:
- aggregated_subsets[key] += subsets[key]
- # Shuffle if data comes from multiple datasets
- random.shuffle(aggregated_subsets[key])
- else:
- aggregated_subsets[key] = subsets[key]
- subsets = aggregated_subsets
-
- # Add post_transforms after t_transforms for the train set
- t_transforms += post_transforms
-
- for key in subsets.keys():
- retval[key] = make_subset(
- subsets[key], transforms=transforms, suffixes=post_transforms
- )
- if key == "train":
- retval["__train__"] = make_subset(
- subsets[key], transforms=transforms, suffixes=(t_transforms)
- )
- if key == "validation":
- # also use it for validation during training
- retval["__valid__"] = retval[key]
-
- if (
- ("__train__" in retval)
- and ("train" in retval)
- and ("__valid__" not in retval)
- ):
- # if the dataset does not have a validation set, we use the unaugmented
- # training set as validation set
- retval["__valid__"] = retval["train"]
-
- return retval
-
-
-def get_samples_weights(dataset):
- """Compute the weights of all the samples of the dataset to balance it
- using the sampler of the dataloader.
-
- This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`
- and computes the weights to balance each class in the dataset and the
- datasets themselves if we have a ConcatDataset.
-
-
- Parameters
- ----------
-
- dataset : torch.utils.data.dataset.Dataset
- An instance of torch.utils.data.dataset.Dataset
- ConcatDataset are supported
-
-
- Returns
- -------
-
- samples_weights : :py:class:`torch.Tensor`
- the weights for all the samples in the dataset given as input
- """
- samples_weights = []
-
- if isinstance(dataset, torch.utils.data.ConcatDataset):
- for ds in dataset.datasets:
- # Weighting only for binary labels
- if isinstance(ds._samples[0].label, int):
- # Groundtruth
- targets = []
- for s in ds._samples:
- targets.append(s.label)
- targets = torch.tensor(targets)
-
- # Count number of samples per class
- class_sample_count = torch.tensor(
- [
- (targets == t).sum()
- for t in torch.unique(targets, sorted=True)
- ]
- )
-
- weight = 1.0 / class_sample_count.float()
-
- samples_weights.append(
- torch.tensor([weight[t] for t in targets])
- )
-
- else:
- # We only weight to sample equally from each dataset
- samples_weights.append(torch.full((len(ds),), 1.0 / len(ds)))
-
- # Concatenate sample weights from all the datasets
- samples_weights = torch.cat(samples_weights)
-
- else:
- # Weighting only for binary labels
- if isinstance(dataset._samples[0].label, int):
- # Groundtruth
- targets = []
- for s in dataset._samples:
- targets.append(s.label)
- targets = torch.tensor(targets)
-
- # Count number of samples per class
- class_sample_count = torch.tensor(
- [
- (targets == t).sum()
- for t in torch.unique(targets, sorted=True)
- ]
- )
-
- weight = 1.0 / class_sample_count.float()
-
- samples_weights = torch.tensor([weight[t] for t in targets])
-
- else:
- # Equal weights for non-binary labels
- samples_weights = torch.ones(len(dataset._samples))
-
- return samples_weights
-
-
-def get_positive_weights(dataset):
- """Compute the positive weights of each class of the dataset to balance the
- BCEWithLogitsLoss criterion.
-
- This function takes as input a :py:class:`torch.utils.data.dataset.Dataset`
- and computes the positive weights of each class to use them to have
- a balanced loss.
-
-
- Parameters
- ----------
-
- dataset : torch.utils.data.dataset.Dataset
- An instance of torch.utils.data.dataset.Dataset
- ConcatDataset are supported
-
-
- Returns
- -------
-
- positive_weights : :py:class:`torch.Tensor`
- the positive weight of each class in the dataset given as input
- """
- targets = []
-
- if isinstance(dataset, torch.utils.data.ConcatDataset):
- for ds in dataset.datasets:
- for s in ds._samples:
- targets.append(s.label)
-
- else:
- for s in dataset._samples:
- targets.append(s.label)
-
- targets = torch.tensor(targets)
-
- # Binary labels
- if len(list(targets.shape)) == 1:
- class_sample_count = [
- float((targets == t).sum().item())
- for t in torch.unique(targets, sorted=True)
- ]
-
- # Divide negatives by positives
- positive_weights = torch.tensor(
- [class_sample_count[0] / class_sample_count[1]]
- ).reshape(-1)
-
- # Multiclass labels
- else:
- class_sample_count = torch.sum(targets, dim=0)
- negative_class_sample_count = (
- torch.full((targets.size()[1],), float(targets.size()[0]))
- - class_sample_count
- )
-
- positive_weights = negative_class_sample_count / (
- class_sample_count + negative_class_sample_count
- )
-
- return positive_weights
-
-
-def return_subsets(dataset):
- train_dataset = None
- validation_dataset = None
- extra_validation_datasets = None
- predict_dataset = None
-
- if "__train__" in dataset:
- logger.info("Found (dedicated) '__train__' set for training")
- train_dataset = dataset["__train__"]
- else:
- train_dataset = dataset["train"]
-
- if "__valid__" in dataset:
- logger.info("Found (dedicated) '__valid__' set for validation")
- validation_dataset = dataset["__valid__"]
-
- if "__extra_valid__" in dataset:
- if not isinstance(dataset["__extra_valid__"], list):
- raise RuntimeError(
- f"If present, dataset['__extra_valid__'] must be a list, "
- f"but you passed a {type(dataset['__extra_valid__'])}, "
- f"which is invalid."
- )
- logger.info(
- f"Found {len(dataset['__extra_valid__'])} extra validation "
- f"set(s) to be tracked during training"
- )
- logger.info(
- "Extra validation sets are NOT used for model checkpointing!"
- )
- extra_validation_datasets = dataset["__extra_valid__"]
- else:
- extra_validation_datasets = None
-
- predict_dataset = dataset
-
- return (
- train_dataset,
- validation_dataset,
- extra_validation_datasets,
- predict_dataset,
- )
diff --git a/src/ptbench/data/shenzhen/default.py b/src/ptbench/data/shenzhen/default.py
index 3801efbc2a5f66d467a7681be37819e7fda74ede..bbeabcafa98554f6f8bcae71df287e6c3eda939e 100644
--- a/src/ptbench/data/shenzhen/default.py
+++ b/src/ptbench/data/shenzhen/default.py
@@ -12,8 +12,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_0.py b/src/ptbench/data/shenzhen/fold_0.py
index d65d513bcb4cefcfc553e3c6bde864392db01add..5b4d45602d13a0e6bd0e2724a6c3202c1532eef6 100644
--- a/src/ptbench/data/shenzhen/fold_0.py
+++ b/src/ptbench/data/shenzhen/fold_0.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_0_rgb.py b/src/ptbench/data/shenzhen/fold_0_rgb.py
index bcc853dd7c52543d4aa27a4b10083d4407582786..143ef731fae0f7d3c746e384e08e400f36c92511 100644
--- a/src/ptbench/data/shenzhen/fold_0_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_0_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_1.py b/src/ptbench/data/shenzhen/fold_1.py
index b9494f156e69ef9ed2af68d607f8d8db2540381f..f01adef0af4da152ad756036aacb4bf83c5c20cc 100644
--- a/src/ptbench/data/shenzhen/fold_1.py
+++ b/src/ptbench/data/shenzhen/fold_1.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_1_rgb.py b/src/ptbench/data/shenzhen/fold_1_rgb.py
index 01e23967e51878a348b89d14e263ff2c5e1d0503..9d457adfa8835e45c7d5dc993ab23ffc8baafeb9 100644
--- a/src/ptbench/data/shenzhen/fold_1_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_1_rgb.py
@@ -12,8 +12,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_2.py b/src/ptbench/data/shenzhen/fold_2.py
index 8d5cf816ce4ae950f19a71ea0c57662f427e2ee4..04dd656263cc6e71d2c61e3f5e221f7129fa7035 100644
--- a/src/ptbench/data/shenzhen/fold_2.py
+++ b/src/ptbench/data/shenzhen/fold_2.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_2_rgb.py b/src/ptbench/data/shenzhen/fold_2_rgb.py
index baf6752d66093a059f295f0e540a9d3ffd6b681f..37cbe10ebd72057535bacd241ecb4dbfb2bded3d 100644
--- a/src/ptbench/data/shenzhen/fold_2_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_2_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_3.py b/src/ptbench/data/shenzhen/fold_3.py
index b42882ccf663b2635dbc106b817f1956a113863e..b43fcb29c9392d6e9bc5c20d9e93969b33bd2708 100644
--- a/src/ptbench/data/shenzhen/fold_3.py
+++ b/src/ptbench/data/shenzhen/fold_3.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_3_rgb.py b/src/ptbench/data/shenzhen/fold_3_rgb.py
index a02c2b1d4e7b33321aa45d537f9af2155f4ae321..162a3f82d640633ce6d13d25b2a500dc0fb63a54 100644
--- a/src/ptbench/data/shenzhen/fold_3_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_3_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_4.py b/src/ptbench/data/shenzhen/fold_4.py
index a9ad14710ad37c02c34e6575fbac064db64a4054..58e0a2f221e0a6a48b3466c015c9c5790fc6ae3a 100644
--- a/src/ptbench/data/shenzhen/fold_4.py
+++ b/src/ptbench/data/shenzhen/fold_4.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_4_rgb.py b/src/ptbench/data/shenzhen/fold_4_rgb.py
index 3620ba428533a9e97392bde40f77b55b454809bb..0dd4ccf89c553a19030774772447bb66bf0cf9b7 100644
--- a/src/ptbench/data/shenzhen/fold_4_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_4_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_5.py b/src/ptbench/data/shenzhen/fold_5.py
index 426a9d6609e212168b3c2775b791de416a6b7dfd..ff115340f723d33ebdeb49524cc7e3b269738563 100644
--- a/src/ptbench/data/shenzhen/fold_5.py
+++ b/src/ptbench/data/shenzhen/fold_5.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_5_rgb.py b/src/ptbench/data/shenzhen/fold_5_rgb.py
index 29e7013885ac1ba917ce40c7a027c827af54d705..46e255e7c37c1f547d25626970324f88d58ba03e 100644
--- a/src/ptbench/data/shenzhen/fold_5_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_5_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_6.py b/src/ptbench/data/shenzhen/fold_6.py
index fb0a91b896723240f26a283cb76c5a2245470fc0..eb81ae882369a1a99bca60e93b1922c7a9d7e9fc 100644
--- a/src/ptbench/data/shenzhen/fold_6.py
+++ b/src/ptbench/data/shenzhen/fold_6.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_6_rgb.py b/src/ptbench/data/shenzhen/fold_6_rgb.py
index 35e7e6d7e7af0758aa4aaa01f24887e3eea6711f..b9654d08008bd10f0a5d953f2a79870214218651 100644
--- a/src/ptbench/data/shenzhen/fold_6_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_6_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_7.py b/src/ptbench/data/shenzhen/fold_7.py
index 743b344d77b9324611e1490dc47966bb52dc41a4..79b0d1fff4483ef98eed42b6dce6c6b64076bd1e 100644
--- a/src/ptbench/data/shenzhen/fold_7.py
+++ b/src/ptbench/data/shenzhen/fold_7.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_7_rgb.py b/src/ptbench/data/shenzhen/fold_7_rgb.py
index 0a9f83d7a0072aa722a7d1cb881249e4722be36c..8a36acb2c79a4467f5dccad532c17cf528613ab5 100644
--- a/src/ptbench/data/shenzhen/fold_7_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_7_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_8.py b/src/ptbench/data/shenzhen/fold_8.py
index ee7f716714adc35beeab1b4eb450253d408320ad..cf1cd36a8d2d2a40113a775131f9d5b8ba0a092a 100644
--- a/src/ptbench/data/shenzhen/fold_8.py
+++ b/src/ptbench/data/shenzhen/fold_8.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_8_rgb.py b/src/ptbench/data/shenzhen/fold_8_rgb.py
index 1351790faf6234c2d7a3b51c500b2d16c94fd2c0..1aa0bcec76d875d8e9b966b4461228230bdf79f2 100644
--- a/src/ptbench/data/shenzhen/fold_8_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_8_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_9.py b/src/ptbench/data/shenzhen/fold_9.py
index dbd8ab3146dca6bda7048c1d47fe1ee0852129c7..e1bb569d0389a8eb67b0ad186e48c6f98aa7cf57 100644
--- a/src/ptbench/data/shenzhen/fold_9.py
+++ b/src/ptbench/data/shenzhen/fold_9.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/fold_9_rgb.py b/src/ptbench/data/shenzhen/fold_9_rgb.py
index 729141dc3a134e4d5e815441262a58004b98ed80..c0a577df0a0d3667420c32042816f65ba9ad20ce 100644
--- a/src/ptbench/data/shenzhen/fold_9_rgb.py
+++ b/src/ptbench/data/shenzhen/fold_9_rgb.py
@@ -11,8 +11,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
diff --git a/src/ptbench/data/shenzhen/rgb.py b/src/ptbench/data/shenzhen/rgb.py
index 6186990ec8023df5030ba84a9be627e80a95ba44..7bdb8fe3ce6826fb98d0d6356f2e1b429670a3d1 100644
--- a/src/ptbench/data/shenzhen/rgb.py
+++ b/src/ptbench/data/shenzhen/rgb.py
@@ -12,8 +12,9 @@
from clapper.logging import setup
+from .. import return_subsets
from ..base_datamodule import BaseDataModule
-from . import _maker, return_subsets
+from . import _maker
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")