def create_generic_scenario_from_filelists(
root: Union[str, Path],
train_file_lists: Sequence[Union[str, Path]],
test_file_lists: Union[Union[str, Path], Sequence[Union[str, Path]]],
task_labels: Sequence[int],
complete_test_set_only: bool = False,
train_transform=None,
train_target_transform=None,
eval_transform=None,
eval_target_transform=None,
) -> GenericCLScenario:
"""
This helper function is DEPRECATED in favor of
`create_generic_benchmark_from_filelists`.
Creates a generic scenario given a list of filelists and the respective task
labels. A separate dataset will be created for each filelist and each of
those training datasets will be considered a separate training experience.
In its base form, this function accepts a list of filelists for the test
datsets that must contain the same amount of elements of the training list.
Those pairs of datasets are then used to create the "past", "cumulative"
(a.k.a. growing) and "future" test sets. However, in certain Continual
Learning scenarios only the concept of "complete" test set makes sense. In
that case, the ``complete_test_set_only`` should be set to True (see the
parameter description for more info).
This helper functions is the best shot when loading Caffe-style dataset
based on filelists.
The resulting benchmark instance and the intermediate datasets used to
populate it will be of type CLASSIFICATION.
:param root: The root path of the dataset.
:param train_file_lists: A list of filelists describing the
paths of the training patterns for each experience.
:param test_file_lists: A list of filelists describing the
paths of the test patterns for each experience.
:param task_labels: A list of task labels. Must contain the same amount of
elements of the ``train_file_lists`` parameter. For
Single-Incremental-Task (a.k.a. Task-Free) scenarios, this is usually
a list of zeros. For Multi Task scenario, this is usually a list of
ascending task labels (starting from 0).
:param complete_test_set_only: If True, only the complete test set will
be returned by the scenario. This means that the ``test_file_lists``
parameter must be list with a single element (the complete test set).
Alternatively, can be a plain string or :class:`Path` object.
Defaults to False, which means that ``train_file_lists`` and
``test_file_lists`` must contain the same amount of filelists paths.
:param train_transform: The transformation to apply to the training data,
e.g. a random crop, a normalization or a concatenation of different
transformations (see torchvision.transform documentation for a
comprehensive list of possible transformations). Defaults to None.
:param train_target_transform: The transformation to apply to training
patterns targets. Defaults to None.
:param eval_transform: The transformation to apply to the test data,
e.g. a random crop, a normalization or a concatenation of different
transformations (see torchvision.transform documentation for a
comprehensive list of possible transformations). Defaults to None.
:param eval_target_transform: The transformation to apply to test
patterns targets. Defaults to None.
:returns: A :class:`GenericCLScenario` instance.
"""
warnings.warn(
"create_generic_scenario_from_filelists is deprecated in "
"favor of create_generic_benchmark_from_filelists.",
DeprecationWarning,
)
train_datasets, test_dataset = datasets_from_filelists(
root,
train_file_lists,
test_file_lists,
complete_test_set_only=complete_test_set_only,
)
return create_multi_dataset_generic_scenario(
train_datasets,
test_dataset,
task_labels,
train_transform=train_transform,
train_target_transform=train_target_transform,
eval_transform=eval_transform,
eval_target_transform=eval_target_transform,
complete_test_set_only=complete_test_set_only,
dataset_type=AvalancheDatasetType.CLASSIFICATION,
)
def create_generic_scenario_from_filelists(
root: Union[str, Path],
train_file_lists: Sequence[Union[str, Path]],
test_file_lists: Union[Union[str, Path], Sequence[Union[str, Path]]],
task_labels: Sequence[int],
complete_test_set_only: bool = False,
train_transform=None,
train_target_transform=None,
test_transform=None,
test_target_transform=None) -> GenericCLScenario:
"""
Creates a generic scenario given a list of filelists and the respective task
labels. A separate dataset will be created for each filelist and each of
those training datasets will be considered a separate training experience.
Contents of the datasets will not be changed, including the targets.
In its base form, this function accepts a list of filelists for the test
datsets that must contain the same amount of elements of the training list.
Those pairs of datasets are then used to create the "past", "cumulative"
(a.k.a. growing) and "future" test sets. However, in certain Continual
Learning scenarios only the concept of "complete" test set makes sense. In
that case, the ``complete_test_set_only`` should be set to True (see the
parameter description for more info).
This helper functions is the best shot when loading Caffe-style dataset
based on filelists.
:param root: The root path of the dataset.
:param train_file_lists: A list of filelists describing the
paths of the training patterns for each experience.
:param test_file_lists: A list of filelists describing the
paths of the test patterns for each experience.
:param task_labels: A list of task labels. Must contain the same amount of
elements of the ``train_file_lists`` parameter. For
Single-Incremental-Task (a.k.a. Task-Free) scenarios, this is usually
a list of zeros. For Multi Task scenario, this is usually a list of
ascending task labels (starting from 0).
:param complete_test_set_only: If True, only the complete test set will
be returned by the scenario. This means that the ``test_file_lists``
parameter must be list with a single element (the complete test set).
Alternatively, can be a plain string or :class:`Path` object.
Defaults to False, which means that ``train_file_lists`` and
``test_file_lists`` must contain the same amount of filelists paths.
:param train_transform: The transformation to apply to training patterns.
Defaults to None.
:param train_target_transform: The transformation to apply to training
patterns targets. Defaults to None.
:param test_transform: The transformation to apply to test patterns.
Defaults to None.
:param test_target_transform: The transformation to apply to test
patterns targets. Defaults to None.
:returns: A :class:`GenericCLScenario` instance.
"""
train_datasets, test_dataset = datasets_from_filelists(
root,
train_file_lists,
test_file_lists,
complete_test_set_only=complete_test_set_only,
train_transform=train_transform,
train_target_transform=train_target_transform,
test_transform=test_transform,
test_target_transform=test_target_transform)
return create_multi_dataset_generic_scenario(
train_datasets,
test_dataset,
task_labels,
complete_test_set_only=complete_test_set_only)