62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""The implementation of `tf.data.Dataset.filter`."""
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.ops import gen_dataset_ops
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def _filter(input_dataset, predicate, name=None): # pylint: disable=redefined-builtin
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return _FilterDataset(input_dataset, predicate, name=name)
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class _FilterDataset(dataset_ops.UnaryUnchangedStructureDataset):
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"""A `Dataset` that filters its input according to a predicate function."""
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def __init__(self,
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input_dataset,
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predicate,
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use_legacy_function=False,
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name=None):
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"""See `Dataset.filter` for details."""
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self._input_dataset = input_dataset
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wrapped_func = structured_function.StructuredFunctionWrapper(
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predicate,
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self._transformation_name(),
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dataset=input_dataset,
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use_legacy_function=use_legacy_function)
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if not wrapped_func.output_structure.is_compatible_with(
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tensor_spec.TensorSpec([], dtypes.bool)):
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raise ValueError(f"Invalid `predicate`. `predicate` must return a "
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f"`tf.bool` scalar tensor, but its return type is "
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f"{wrapped_func.output_structure}.")
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self._predicate = wrapped_func
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self._name = name
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variant_tensor = gen_dataset_ops.filter_dataset(
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input_dataset._variant_tensor, # pylint: disable=protected-access
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other_arguments=self._predicate.function.captured_inputs,
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predicate=self._predicate.function,
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**self._common_args)
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super().__init__(input_dataset, variant_tensor)
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def _functions(self):
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return [self._predicate]
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def _transformation_name(self):
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return "Dataset.filter()"
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