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Support
tfds.features.Optional in TFDS for tfds.data_source users.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,149 @@ | ||
| # coding=utf-8 | ||
| # Copyright 2023 The TensorFlow Datasets Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| """Optional feature connector.""" | ||
|
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||
| from __future__ import annotations | ||
|
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| from typing import Any | ||
|
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| from tensorflow_datasets.core.features import feature as feature_lib | ||
| from tensorflow_datasets.core.features import features_dict | ||
| from tensorflow_datasets.core.features import tensor_feature | ||
| from tensorflow_datasets.core.proto import feature_pb2 | ||
| from tensorflow_datasets.core.utils import py_utils | ||
| from tensorflow_datasets.core.utils import type_utils | ||
|
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| Json = type_utils.Json | ||
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| class Optional(feature_lib.FeatureConnector): | ||
| r"""Optional feature connector. | ||
| Warning: This feature is under active development. For now, | ||
| `tfds.features.Optional` can only wrap `tfds.features.Scalar`: | ||
| ```python | ||
| features = tfds.features.FeaturesDict({ | ||
| 'myfeature': tfds.features.Optional(np.int32), | ||
| }) | ||
| ``` | ||
| In `_generate_examples`, you can yield `None` instead of the actual feature | ||
| value: | ||
| ```python | ||
| def _generate_examples(self): | ||
| yield { | ||
| 'myfeature': None, # Accept `None` or feature | ||
| } | ||
| ``` | ||
| For Grain/NumPy users, `tfds.data_source` will output None values. | ||
| ```python | ||
| ds = tfds.data_source('dataset_with_optional', split='train') | ||
| for element in ds: | ||
| if element is None: | ||
| # ... | ||
| else: | ||
| # ... | ||
| ``` | ||
| For tf.data users, None values don't make sense in tensors, so we haven't | ||
| implemented the feature yet. If you're a tf.data user with a use case for | ||
| optional values, we'd like to hear from you. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| feature: feature_lib.FeatureConnectorArg, | ||
| *, | ||
| doc: feature_lib.DocArg = None, | ||
| ): | ||
| """Constructor. | ||
| Args: | ||
| feature: The feature to wrap (any TFDS feature is supported). | ||
| doc: Documentation of this feature (e.g. description). | ||
| """ | ||
| self._feature = features_dict.to_feature(feature) | ||
| if not isinstance(self._feature, tensor_feature.Tensor): | ||
| raise NotImplementedError( | ||
| 'tfds.features.Optional only supports Tensors. Refer to its' | ||
| ' documentation for more information.' | ||
| ) | ||
| super().__init__(doc=doc) | ||
|
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| @py_utils.memoize() | ||
| def get_tensor_info(self): | ||
| """See base class for details.""" | ||
| return self._feature.get_tensor_info() | ||
|
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| @py_utils.memoize() | ||
| def get_serialized_info(self): | ||
| """See base class for details.""" | ||
| return self._feature.get_serialized_info() | ||
|
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| def __getitem__(self, key: str) -> feature_lib.FeatureConnector: | ||
| """Allows to access the underlying features directly.""" | ||
| return self._feature[key] | ||
|
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| def __contains__(self, key: str) -> bool: | ||
| return key in self._feature | ||
|
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| def save_metadata(self, *args, **kwargs): | ||
| """See base class for details.""" | ||
| self._feature.save_metadata(*args, **kwargs) | ||
|
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| def load_metadata(self, *args, **kwargs): | ||
| """See base class for details.""" | ||
| self._feature.load_metadata(*args, **kwargs) | ||
|
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| @classmethod | ||
| def from_json_content(cls, value: feature_pb2.Optional) -> Optional: | ||
| """See base class for details.""" | ||
| feature = feature_lib.FeatureConnector.from_proto(value.feature) | ||
| return cls(feature) | ||
|
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| def to_json_content(self) -> feature_pb2.Optional: | ||
| """See base class for details.""" | ||
| return feature_pb2.Optional(feature=self._feature.to_proto()) | ||
|
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| @property | ||
| def feature(self): | ||
| """The inner feature.""" | ||
| return self._feature | ||
|
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| def encode_example(self, example: Any) -> Any: | ||
| """See base class for details.""" | ||
| if example is None: | ||
| return None | ||
| else: | ||
| return self._feature.encode_example(example) | ||
|
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| def decode_example(self, example: Any) -> Any: | ||
| """See base class for details.""" | ||
| raise NotImplementedError( | ||
| 'tfds.features.Optional only supports tfds.data_source.' | ||
| ) | ||
|
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| def decode_example_np( | ||
| self, example: Any | ||
| ) -> type_utils.NpArrayOrScalar | None: | ||
| if example is None: | ||
| return None | ||
| else: | ||
| return self._feature.decode_example_np(example) |
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80
tensorflow_datasets/core/features/optional_feature_test.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,80 @@ | ||
| # coding=utf-8 | ||
| # Copyright 2023 The TensorFlow Datasets Authors. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| """Tests for optional_feature.""" | ||
|
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| from absl.testing import parameterized | ||
| import numpy as np | ||
| from tensorflow_datasets import testing | ||
| from tensorflow_datasets.core import features | ||
|
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|
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| class ScalarFeatureTest( | ||
| parameterized.TestCase, testing.FeatureExpectationsTestCase | ||
| ): | ||
|
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| @parameterized.parameters( | ||
| (np.int64, 42, 42), | ||
| (np.int64, None, testing.TestValue.NONE), | ||
| (np.str_, 'foo', 'foo'), | ||
| (np.str_, None, testing.TestValue.NONE), | ||
| ) | ||
| def test_scalar(self, dtype, value, expected_np): | ||
| self.assertFeature( | ||
| feature=features.Optional( | ||
| features.Scalar(dtype=dtype, doc='Some description') | ||
| ), | ||
| shape=(), | ||
| dtype=dtype, | ||
| tests=[ | ||
| testing.FeatureExpectationItem( | ||
| value=value, | ||
| raise_cls=NotImplementedError, | ||
| raise_msg='supports tfds.data_source', | ||
| ), | ||
| testing.FeatureExpectationItem( | ||
| value=value, | ||
| expected_np=expected_np, | ||
| ), | ||
| ], | ||
| ) | ||
|
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| def test_dict(self): | ||
| self.assertFeature( | ||
| feature=features.FeaturesDict({'a': features.Optional(np.int32)}), | ||
| shape={'a': ()}, | ||
| dtype={'a': np.int32}, | ||
| tests=[ | ||
| testing.FeatureExpectationItem( | ||
| value={'a': None}, | ||
| raise_cls=NotImplementedError, | ||
| raise_msg='supports tfds.data_source', | ||
| ), | ||
| testing.FeatureExpectationItem( | ||
| value={'a': None}, | ||
| expected_np={'a': None}, | ||
| ), | ||
| # You cannot ommit the key, you do have to specify {'a': None}. | ||
| testing.FeatureExpectationItem( | ||
| value={}, | ||
| raise_cls_np=RuntimeError, | ||
| raise_msg="'a'", | ||
| ), | ||
| ], | ||
| ) | ||
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| if __name__ == '__main__': | ||
| testing.test_main() |
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