Removed maker functions

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.