# Copyright (C) 2020-2023 Intel Corporation
#
# SPDX-License-Identifier: MIT
from __future__ import annotations
import inspect
import logging as log
import os
import os.path as osp
import warnings
from contextlib import contextmanager
from copy import copy, deepcopy
from typing import (
Any,
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
Set,
Tuple,
Type,
Union,
overload,
)
from datumaro.components.annotation import Annotation, AnnotationType, LabelCategories
from datumaro.components.config_model import Source
from datumaro.components.dataset_base import (
DEFAULT_SUBSET_NAME,
CategoriesInfo,
DatasetBase,
DatasetInfo,
DatasetItem,
IDataset,
)
from datumaro.components.dataset_item_storage import DatasetItemStorageDatasetView
from datumaro.components.dataset_storage import DatasetPatch, DatasetStorage, StreamDatasetStorage
from datumaro.components.environment import DEFAULT_ENVIRONMENT, Environment
from datumaro.components.errors import (
DatasetImportError,
MultipleFormatsMatchError,
NoMatchingFormatsError,
StreamedItemError,
UnknownFormatError,
)
from datumaro.components.exporter import ExportContext, Exporter, ExportErrorPolicy, _ExportFail
from datumaro.components.filter import (
UserFunctionAnnotationsFilter,
UserFunctionDatasetFilter,
XPathAnnotationsFilter,
XPathDatasetFilter,
)
from datumaro.components.importer import ImportContext, ImportErrorPolicy, _ImportFail
from datumaro.components.launcher import Launcher
from datumaro.components.media import Image, MediaElement
from datumaro.components.merge import DEFAULT_MERGE_POLICY
from datumaro.components.progress_reporting import NullProgressReporter, ProgressReporter
from datumaro.components.transformer import ItemTransform, ModelTransform, Transform
from datumaro.util.log_utils import logging_disabled
from datumaro.util.meta_file_util import load_hash_key
from datumaro.util.os_util import rmtree
from datumaro.util.scope import on_error_do, scoped
DEFAULT_FORMAT = "datumaro"
__all__ = ["Dataset", "eager_mode"]
[docs]
class DatasetSubset(IDataset): # non-owning view
def __init__(self, parent: Dataset, name: str):
super().__init__()
self.parent = parent
self.name = name
def __iter__(self):
yield from self.parent._data.get_subset(self.name)
def __len__(self):
subset: DatasetItemStorageDatasetView.Subset = self.parent._data.get_subset(self.name)
return len(subset)
[docs]
def put(self, item):
return self.parent.put(item, subset=self.name)
[docs]
def get(self, id, subset=None):
assert (subset or DEFAULT_SUBSET_NAME) == (self.name or DEFAULT_SUBSET_NAME)
return self.parent.get(id, subset=self.name)
[docs]
def remove(self, id, subset=None):
assert (subset or DEFAULT_SUBSET_NAME) == (self.name or DEFAULT_SUBSET_NAME)
return self.parent.remove(id, subset=self.name)
[docs]
def get_subset(self, name):
assert (name or DEFAULT_SUBSET_NAME) == (self.name or DEFAULT_SUBSET_NAME)
return self
[docs]
def subsets(self):
if (self.name or DEFAULT_SUBSET_NAME) == DEFAULT_SUBSET_NAME:
return self.parent.subsets()
return {self.name: self}
[docs]
def infos(self):
return self.parent.infos()
[docs]
def categories(self):
return self.parent.categories()
[docs]
def ann_types(self):
return self.parent.ann_types()
[docs]
def get_annotated_items(self):
return sum(bool(s.annotations) for s in self.parent._data.get_subset(self.name))
[docs]
def get_annotations(self):
annotations_by_type = {t.name: {"count": 0} for t in AnnotationType}
for item in self.parent._data.get_subset(self.name):
for ann in item.annotations:
annotations_by_type[ann.type.name]["count"] += 1
return sum(t["count"] for t in annotations_by_type.values())
[docs]
def as_dataset(self) -> Dataset:
dataset = Dataset.from_extractors(self, env=self.parent.env)
dataset._format = self.parent._format
dataset._source_path = self.parent._source_path
return dataset
[docs]
class Dataset(IDataset):
"""
Represents a dataset, contains metainfo about labels and dataset items.
Provides iteration and access options to dataset elements.
By default, all operations are done lazily, it can be changed by
modifying the `eager` property and by using the `eager_mode`
context manager.
Dataset is supposed to have a single media type for its items. If the
dataset is filled manually or from extractors, and media type does not
match, an error is raised.
"""
_global_eager: bool = False
_stream = False
[docs]
@classmethod
def from_iterable(
cls,
iterable: Iterable[DatasetItem],
infos: Optional[DatasetInfo] = None,
categories: Union[CategoriesInfo, List[str], None] = None,
*,
env: Optional[Environment] = None,
media_type: Type[MediaElement] = Image,
ann_types: Optional[Set[AnnotationType]] = [],
) -> Dataset:
"""
Creates a new dataset from an iterable object producing dataset items -
a generator, a list etc. It is a convenient way to create and fill
a custom dataset.
Parameters:
iterable: An iterable which returns dataset items
infos: A dictionary of the dataset specific information
categories: A simple list of labels or complete information
about labels. If not specified, an empty list of labels
is assumed.
media_type: Media type for the dataset items. If the sequence
contains items with mismatching media type, an error is
raised during caching
env: A context for plugins, which will be used for this dataset.
If not specified, the builtin plugins will be used.
Returns:
dataset: A new dataset with specified contents
"""
if infos is None:
infos = {}
# TODO: remove the default value for media_type
# https://github.com/openvinotoolkit/datumaro/issues/675
if isinstance(categories, list):
categories = {AnnotationType.label: LabelCategories.from_iterable(categories)}
if not categories:
categories = {}
class _extractor(DatasetBase):
def __init__(self):
super().__init__(
length=len(iterable) if hasattr(iterable, "__len__") else None,
media_type=media_type,
ann_types=ann_types,
)
def __iter__(self):
return iter(iterable)
def infos(self):
return infos
def categories(self):
return categories
return cls.from_extractors(_extractor(), env=env)
def __init__(
self,
source: Optional[IDataset] = None,
*,
infos: Optional[DatasetInfo] = None,
categories: Optional[CategoriesInfo] = None,
media_type: Optional[Type[MediaElement]] = None,
ann_types: Optional[Set[AnnotationType]] = None,
env: Optional[Environment] = None,
) -> None:
super().__init__()
assert env is None or isinstance(env, Environment), env
self._env = env
self.eager = None
self._data = DatasetStorage(
source=source,
infos=infos,
categories=categories,
media_type=media_type,
ann_types=ann_types,
)
if self.is_eager:
self.init_cache()
self._format = DEFAULT_FORMAT
self._source_path = None
self._options = {}
def __repr__(self) -> str:
separator = "\t"
return (
f"Dataset\n"
f"\tsize={len(self._data)}\n"
f"\tsource_path={self._source_path}\n"
f"\tmedia_type={self.media_type()}\n"
f"\tann_types={self.ann_types()}\n"
f"\tannotated_items_count={self.get_annotated_items()}\n"
f"\tannotations_count={self.get_annotations()}\n"
f"subsets\n"
f"\t{separator.join(self.get_subset_info())}"
f"infos\n"
f"\t{separator.join(self.get_infos())}"
f"categories\n"
f"\t{separator.join(self.get_categories_info())}"
)
[docs]
def define_infos(self, infos: DatasetInfo) -> None:
self._data.define_infos(infos)
[docs]
def define_categories(self, categories: CategoriesInfo) -> None:
self._data.define_categories(categories)
[docs]
def init_cache(self) -> None:
self._data.init_cache()
def __iter__(self) -> Iterator[DatasetItem]:
yield from self._data
def __len__(self) -> int:
return len(self._data)
[docs]
def get_subset(self, name) -> DatasetSubset:
return DatasetSubset(self, name)
[docs]
def subsets(self) -> Dict[str, DatasetSubset]:
return {k: self.get_subset(k) for k in self._data.subsets()}
[docs]
def infos(self) -> DatasetInfo:
return self._data.infos()
[docs]
def categories(self) -> CategoriesInfo:
return self._data.categories()
[docs]
def ann_types(self) -> Set[AnnotationType]:
return self._data.ann_types()
[docs]
def get(self, id: str, subset: Optional[str] = None) -> Optional[DatasetItem]:
return self._data.get(id, subset)
[docs]
def get_annotated_items(self):
return self._data.get_annotated_items()
[docs]
def get_annotations(self):
return self._data.get_annotations()
[docs]
def get_datasetitem_by_path(self, path):
if self._source_path not in path:
path = osp.join(self._source_path, path)
return self._data.get_datasetitem_by_path(path)
[docs]
def get_label_cat_names(self):
return [
label.name
for label in self._data.categories().get(AnnotationType.label, LabelCategories())
]
[docs]
def get_subset_info(self) -> str:
return (
f"{subset_name}: # of items={len(self.get_subset(subset_name))}, "
f"# of annotated items={self.get_subset(subset_name).get_annotated_items()}, "
f"# of annotations={self.get_subset(subset_name).get_annotations()}\n"
for subset_name in sorted(self.subsets().keys())
)
[docs]
def get_infos(self) -> Tuple[str]:
if self.infos() is not None:
return (f"{k}: {v}\n" for k, v in self.infos().items())
else:
return ("\n",)
[docs]
def get_categories_info(self) -> Tuple[str]:
category_dict = {}
for annotation_type, category in self.categories().items():
if isinstance(category, LabelCategories):
category_names = list(category._indices.keys())
category_dict[annotation_type] = category_names
return (
f"{str(annotation_type).split('.')[-1]}: {list(category_dict.get(annotation_type, []))}\n"
for annotation_type in self.categories().keys()
)
def __contains__(self, x: Union[DatasetItem, str, Tuple[str, str]]) -> bool:
if isinstance(x, DatasetItem):
x = (x.id, x.subset)
elif not isinstance(x, (tuple, list)):
x = [x]
return self.get(*x) is not None
[docs]
def put(
self, item: DatasetItem, id: Optional[str] = None, subset: Optional[str] = None
) -> None:
overrides = {}
if id is not None:
overrides["id"] = id
if subset is not None:
overrides["subset"] = subset
if overrides:
item = item.wrap(**overrides)
self._data.put(item)
[docs]
def remove(self, id: str, subset: Optional[str] = None) -> None:
self._data.remove(id, subset)
@overload
def filter(
self,
expr: str,
*,
filter_annotations: bool = False,
remove_empty: bool = False,
) -> Dataset:
"""
Filters out some dataset items or annotations, using a custom filter
expression.
Results are stored in-place. Modifications are applied lazily.
Args:
expr: XPath-formatted filter expression
(e.g. `/item[subset = 'train']`,
`/item/annotation[label = 'cat']`)
filter_annotations: Indicates if the filter should be
applied to items or annotations
remove_empty: When filtering annotations, allows to
exclude empty items from the resulting dataset
Returns: self
"""
...
@overload
def filter(
self,
filter_func: Union[
Callable[[DatasetItem], bool], Callable[[DatasetItem, Annotation], bool]
],
*,
filter_annotations: bool = False,
remove_empty: bool = False,
) -> Dataset:
"""
Filters out some dataset items or annotations, using a user-provided filter
Python function.
Results are stored in-place. Modifications are applied lazily.
Args:
filter_func: User-provided Python function for filtering
filter_annotations: Indicates if the filter should be
applied to items or annotations
remove_empty: When filtering annotations, allows to
exclude empty items from the resulting dataset
Returns: self
Example:
- (`filter_annotations=False`) This is an example of filtering
dataset items with images larger than 1024 pixels::
from datumaro.components.media import Image
def filter_func(item: DatasetItem) -> bool:
h, w = item.media_as(Image).size
return h > 1024 or w > 1024
filtered = dataset.filter(filter_func=filter_func, filter_annotations=False)
# No items with an image height or width greater than 1024
filtered_items = [item for item in filtered]
- (`filter_annotations=True`) This is an example of removing bounding boxes
sized greater than 50% of the image size::
from datumaro.components.media import Image
from datumaro.components.annotation import Annotation, Bbox
def filter_func(item: DatasetItem, ann: Annotation) -> bool:
# If the annotation is not a Bbox, do not filter
if not isinstance(ann, Bbox):
return False
h, w = item.media_as(Image).size
image_size = h * w
bbox_size = ann.h * ann.w
# Accept Bboxes smaller than 50% of the image size
return bbox_size < 0.5 * image_size
filtered = dataset.filter(filter_func=filter_func, filter_annotations=True)
# No bounding boxes with a size greater than 50% of their image
filtered_items = [item for item in filtered]
"""
...
[docs]
def filter(
self,
expr_or_filter_func: Union[
str, Callable[[DatasetItem], bool], Callable[[DatasetItem, Annotation], bool]
],
*,
filter_annotations: bool = False,
remove_empty: bool = False,
) -> Dataset:
if isinstance(expr_or_filter_func, str):
expr = expr_or_filter_func
return (
self.transform(XPathAnnotationsFilter, xpath=expr, remove_empty=remove_empty)
if filter_annotations
else self.transform(XPathDatasetFilter, xpath=expr)
)
elif callable(expr_or_filter_func):
filter_func = expr_or_filter_func
return (
self.transform(
UserFunctionAnnotationsFilter,
filter_func=filter_func,
remove_empty=remove_empty,
)
if filter_annotations
else self.transform(UserFunctionDatasetFilter, filter_func=filter_func)
)
raise TypeError(expr_or_filter_func)
[docs]
def update(self, source: Union[DatasetPatch, IDataset, Iterable[DatasetItem]]) -> Dataset:
"""
Updates items of the current dataset from another dataset or an
iterable (the source). Items from the source overwrite matching
items in the current dataset. Unmatched items are just appended.
If the source is a DatasetPatch, the removed items in the patch
will be removed in the current dataset.
If the source is a dataset, labels are matched. If the labels match,
but the order is different, the annotation labels will be remapped to
the current dataset label order during updating.
Returns: self
"""
self._data.update(source)
return self
[docs]
def run_model(
self,
model: Union[Launcher, Type[ModelTransform]],
*,
batch_size: int = 1,
append_annotation: bool = False,
num_workers: int = 0,
**kwargs,
) -> Dataset:
"""
Applies a model to dataset items' media and produces a dataset with
media and annotations.
Args:
model: The model to be applied to the dataset
batch_size: The number of dataset items processed
simultaneously by the model
append_annotation: Whether append new annotation to existed annotations
num_workers: The number of worker threads to use for parallel inference.
Set to 0 for single-process mode. Default is 0.
**kwargs: Parameters for the model
Returns: self
"""
if isinstance(model, Launcher):
return self.transform(
ModelTransform,
launcher=model,
batch_size=batch_size,
append_annotation=append_annotation,
num_workers=num_workers,
**kwargs,
)
elif inspect.isclass(model) and isinstance(model, ModelTransform):
return self.transform(
model,
batch_size=batch_size,
append_annotation=append_annotation,
num_workers=num_workers,
**kwargs,
)
else:
raise TypeError("Unexpected 'model' argument type: %s" % type(model))
[docs]
def select(self, pred: Callable[[DatasetItem], bool]) -> Dataset:
class _DatasetFilter(ItemTransform):
def transform_item(self, item):
if pred(item):
return item
return None
return self.transform(_DatasetFilter)
@property
def data_path(self) -> Optional[str]:
return self._source_path
@property
def format(self) -> Optional[str]:
return self._format
@property
def options(self) -> Dict[str, Any]:
return self._options
@property
def is_modified(self) -> bool:
return self._data.has_updated_items()
[docs]
def get_patch(self) -> DatasetPatch:
return self._data.get_patch()
@property
def env(self) -> Environment:
if self._env is None:
return DEFAULT_ENVIRONMENT
return self._env
@property
def is_cache_initialized(self) -> bool:
return self._data.is_cache_initialized()
@property
def is_eager(self) -> bool:
return self.eager if self.eager is not None else self._global_eager
@property
def is_bound(self) -> bool:
return bool(self._source_path) and bool(self._format)
[docs]
def bind(
self, path: str, format: Optional[str] = None, *, options: Optional[Dict[str, Any]] = None
) -> None:
"""
Binds the dataset to a speific directory.
Allows to set default saving parameters.
The following saves will be done to this directory by default and will
use the saved parameters.
"""
self._source_path = path
self._format = format or DEFAULT_FORMAT
self._options = options or {}
[docs]
def flush_changes(self):
self._data.flush_changes()
[docs]
@scoped
def export(
self,
save_dir: str,
format: Union[str, Type[Exporter]],
*,
progress_reporter: Optional[ProgressReporter] = None,
error_policy: Optional[ExportErrorPolicy] = None,
**kwargs,
) -> None:
"""
Saves the dataset in some format.
Args:
save_dir: The output directory
format: The desired output format.
If a string is passed, it is treated as a plugin name,
which is searched for in the dataset environment.
progress_reporter: An object to report progress
error_policy: An object to report format-related errors
**kwargs: Parameters for the format
"""
if not save_dir:
raise ValueError("Dataset export path is not specified")
inplace = save_dir == self._source_path and format == self._format
if isinstance(format, str):
exporter = self.env.exporters[format]
else:
exporter = format
if not (inspect.isclass(exporter) and issubclass(exporter, Exporter)):
raise TypeError("Unexpected 'format' argument type: %s" % type(exporter))
save_dir = osp.abspath(save_dir)
if not osp.exists(save_dir):
on_error_do(rmtree, save_dir, ignore_errors=True)
inplace = False
os.makedirs(save_dir, exist_ok=True)
has_ctx_args = progress_reporter is not None or error_policy is not None
if not progress_reporter:
progress_reporter = NullProgressReporter()
assert "ctx" not in kwargs
exporter_kwargs = copy(kwargs)
exporter_kwargs["stream"] = self._stream
exporter_kwargs["ctx"] = ExportContext(
progress_reporter=progress_reporter, error_policy=error_policy
)
try:
if not inplace:
try:
exporter.convert(self, save_dir=save_dir, **exporter_kwargs)
except TypeError as e:
# TODO: for backward compatibility. To be removed after 0.3
if "unexpected keyword argument 'ctx'" not in str(e):
raise
if has_ctx_args:
warnings.warn(
f"It seems that '{format}' exporter "
"does not support progress and error reporting, "
"It will be disabled in datumaro==1.5.0.",
DeprecationWarning,
)
exporter_kwargs.pop("ctx")
exporter.convert(self, save_dir=save_dir, **exporter_kwargs)
else:
try:
exporter.patch(self, self.get_patch(), save_dir=save_dir, **exporter_kwargs)
except TypeError as e:
# TODO: for backward compatibility. To be removed after 0.3
if "unexpected keyword argument 'ctx'" not in str(e):
raise
if has_ctx_args:
warnings.warn(
f"It seems that '{format}' exporter "
"does not support progress and error reporting, "
"It will be disabled in datumaro==1.5.0.",
DeprecationWarning,
)
exporter_kwargs.pop("ctx")
exporter.patch(self, self.get_patch(), save_dir=save_dir, **exporter_kwargs)
except _ExportFail as e:
raise e.__cause__
self.bind(save_dir, format, options=copy(kwargs))
if not self._stream:
self.flush_changes()
[docs]
def save(self, save_dir: Optional[str] = None, **kwargs) -> None:
options = dict(self._options)
options.update(kwargs)
self.export(save_dir or self._source_path, format=self._format, **options)
[docs]
@classmethod
def load(cls, path: str, **kwargs) -> Dataset:
return cls.import_from(path, format=DEFAULT_FORMAT, **kwargs)
[docs]
@classmethod
def import_from(
cls,
path: str,
format: Optional[str] = None,
*,
env: Optional[Environment] = None,
progress_reporter: Optional[ProgressReporter] = None,
error_policy: Optional[ImportErrorPolicy] = None,
**kwargs,
) -> Dataset:
"""
Creates a `Dataset` instance from a dataset on the disk.
Args:
path - The input file or directory path
format - Dataset format.
If a string is passed, it is treated as a plugin name,
which is searched for in the `env` plugin context.
If not set, will try to detect automatically,
using the `env` plugin context.
env - A plugin collection. If not set, the built-in plugins are used
progress_reporter - An object to report progress.
Implies earger loading.
error_policy - An object to report format-related errors.
Implies earger loading.
**kwargs - Parameters for the format
"""
if env is None:
env = DEFAULT_ENVIRONMENT
if not format:
format = cls.detect(path, env=env)
extractor_merger = None
# TODO: remove importers, put this logic into extractors
if format in env.importers:
importer = env.make_importer(format)
with logging_disabled(log.INFO):
detected_sources = (
importer(path, stream=cls._stream, **kwargs)
if importer.can_stream
else importer(path, **kwargs)
)
extractor_merger = importer.get_extractor_merger()
elif format in env.extractors:
detected_sources = [{"url": path, "format": format, "options": kwargs}]
else:
raise UnknownFormatError(format)
# TODO: probably, should not be available in lazy mode, because it
# becomes unreliable and error-prone. For progress reporting it
# makes little sense, because loading stage is spread over other
# operations. Error reporting is going to be unreliable.
has_ctx_args = progress_reporter is not None or error_policy is not None
eager = has_ctx_args
if not progress_reporter:
progress_reporter = NullProgressReporter()
pbars = progress_reporter.split(len(detected_sources))
try:
extractors = []
for src_conf, pbar in zip(detected_sources, pbars):
if not isinstance(src_conf, Source):
src_conf = Source(src_conf)
extractor_kwargs = dict(src_conf.options)
assert "ctx" not in extractor_kwargs
extractor_kwargs["ctx"] = ImportContext(
progress_reporter=pbar, error_policy=error_policy
)
merge_policy = extractor_kwargs.get("merge_policy", DEFAULT_MERGE_POLICY)
try:
extractors.append(
env.make_extractor(src_conf.format, src_conf.url, **extractor_kwargs)
)
except TypeError as e:
# TODO: for backward compatibility. To be removed after 0.3
if "unexpected keyword argument 'ctx'" not in str(e):
raise
if has_ctx_args:
warnings.warn(
f"It seems that '{src_conf.format}' extractor "
"does not support progress and error reporting. "
"It will be disabled in datumaro==1.5.0.",
DeprecationWarning,
)
extractor_kwargs.pop("ctx")
extractors.append(
env.make_extractor(src_conf.format, src_conf.url, **extractor_kwargs)
)
dataset = (
cls(source=extractor_merger(extractors), env=env)
if extractor_merger is not None
else cls.from_extractors(*extractors, env=env, merge_policy=merge_policy)
)
if eager:
dataset.init_cache()
except _ImportFail as e:
cause = e.__cause__ if getattr(e, "__cause__", None) is not None else e
cause.__traceback__ = e.__traceback__
raise DatasetImportError(f"Failed to import dataset '{format}' at '{path}'.") from cause
except Exception as e:
raise DatasetImportError(f"Failed to import dataset '{format}' at '{path}'.") from e
dataset._source_path = path
dataset._format = format
dataset = load_hash_key(path, dataset)
return dataset
[docs]
@staticmethod
def detect(path: str, *, env: Optional[Environment] = None, depth: int = 2) -> str:
"""
Attempts to detect dataset format of a given directory.
This function tries to detect a single format and fails if it's not
possible. Check Environment.detect_dataset() for a function that
reports status for each format checked.
Args:
path: The directory to check
depth: The maximum depth for recursive search
env: A plugin collection. If not set, the built-in plugins are used
"""
if env is None:
env = Environment()
if depth < 0:
raise ValueError("Depth cannot be less than zero")
matches = env.detect_dataset(path, depth=depth)
if not matches:
raise NoMatchingFormatsError()
elif 1 < len(matches):
raise MultipleFormatsMatchError(matches)
else:
return matches[0]
@property
def is_stream(self) -> bool:
return self._data.is_stream
[docs]
def clone(self) -> "Dataset":
"""Create a deep copy of this dataset.
Returns:
A cloned instance of the `Dataset`.
"""
return deepcopy(self)
def __getitem__(self, idx: int) -> DatasetItem:
if not self._data.is_stream:
return self._data[idx]
raise StreamedItemError()
[docs]
class StreamDataset(Dataset):
_stream = True
def __init__(
self,
source: Optional[IDataset] = None,
*,
infos: Optional[DatasetInfo] = None,
categories: Optional[CategoriesInfo] = None,
media_type: Optional[Type[MediaElement]] = None,
ann_types: Optional[Set[AnnotationType]] = None,
env: Optional[Environment] = None,
) -> None:
assert env is None or isinstance(env, Environment), env
self._env = env
self._data = StreamDatasetStorage(
source,
infos=infos,
categories=categories,
media_type=media_type,
ann_types=ann_types,
)
self._format = DEFAULT_FORMAT
self._source_path = None
self._options = {}
@property
def is_eager(self) -> bool:
return False
[docs]
@contextmanager
def eager_mode(new_mode: bool = True, dataset: Optional[Dataset] = None) -> None:
if dataset is not None:
old_mode = dataset.eager
try:
dataset.eager = new_mode
yield
finally:
dataset.eager = old_mode
else:
old_mode = Dataset._global_eager
try:
Dataset._global_eager = new_mode
yield
finally:
Dataset._global_eager = old_mode
