#!/usr/bin/env python
# coding=utf-8
"""Standard configurations for dataset setup"""
from ...data.transforms import ColorJitter as _jitter
from ...data.transforms import RandomHorizontalFlip as _hflip
from ...data.transforms import RandomRotation as _rotation
from ...data.transforms import RandomVerticalFlip as _vflip
RANDOM_ROTATION = [_rotation()]
"""Shared data augmentation based on random rotation only"""
RANDOM_FLIP_JITTER = [_hflip(), _vflip(), _jitter()]
"""Shared data augmentation transforms without random rotation"""
[docs]def make_subset(l, 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
----------
l : 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:`bob.ip.binseg.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(l, prefixes + transforms + suffixes)
[docs]def augment_subset(s, rotation_before=False):
"""Creates a new subset set, **with data augmentation**
Typically, the transforms are chained to a default set of data augmentation
operations (random rotation, horizontal and vertical flips, and color
jitter), but a flag allows prefixing the rotation specially (useful for
some COVD training sets).
.. 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
----------
s : bob.ip.binseg.data.utils.SampleListDataset
A dataset that will be augmented
rotation_before : py:class:`bool`, Optional
A optional flag allowing you to do a rotation augmentation transform
**before** the sequence of transforms for this dataset, that will be
augmented.
Returns
-------
subset : :py:class:`bob.ip.binseg.data.utils.SampleListDataset`
A pre-formatted dataset that can be fed to one of our engines
"""
if rotation_before:
return s.copy(RANDOM_ROTATION + s.transforms + RANDOM_FLIP_JITTER)
return s.copy(s.transforms + RANDOM_ROTATION + RANDOM_FLIP_JITTER)
[docs]def make_dataset(subsets, transforms):
"""Creates a new configuration dataset from dictionary and transforms
This function takes as input a dictionary as those that can be returned by
:py:meth:`bob.ip.binseg.data.dataset.JSONDataset.subsets`, or
:py:meth:`bob.ip.binseg.data.dataset.CSVDataset.subsets`, mapping protocol
names (such as ``train``, ``dev`` and ``test``) to
:py:class:`bob.ip.binseg.data.sample.DelayedSample` lists, and a set of
transforms, and returns a dictionary applying
:py:class:`bob.ip.binseg.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
* ``train``: 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 : dict
A dictionary that contains the delayed sample lists for a number of
named lists. 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. If one of the keys is
``validation``, then this dataset will be also copied to the
``__valid__`` hidden dataset and will be used for validation during
training. Otherwise, if no ``valid`` subset is available, we set
``__valid__`` to be the same as the unaugmented ``train`` subset, if
one is available.
transforms : list
A list of transforms that needs to be applied to all samples in the set
Returns
-------
dataset : dict
A pre-formatted dataset that can be fed to one of our engines. It maps
string names to
:py:class:`bob.ip.binseg.data.utils.SampleListDataset`'s.
"""
retval = {}
for key in subsets.keys():
retval[key] = make_subset(subsets[key], transforms=transforms)
if key == "train":
retval["__train__"] = make_subset(
subsets[key],
transforms=transforms,
suffixes=(RANDOM_ROTATION + RANDOM_FLIP_JITTER),
)
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