diff --git a/src/ptbench/configs/datasets/__init__.py b/src/ptbench/configs/datasets/__init__.py
index 1e4d913188fcbccad5aa0effa18837434f37e49b..88ac7a33e7051a69ff6d8ecfdb55aeb94b13b72f 100644
--- a/src/ptbench/configs/datasets/__init__.py
+++ b/src/ptbench/configs/datasets/__init__.py
@@ -2,8 +2,6 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
-import random
-
import torch
from torchvision.transforms import RandomRotation
@@ -14,151 +12,6 @@ RANDOM_ROTATION = [RandomRotation(15)]
"""Shared data augmentation based on random rotation only."""
-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.