173 lines
7.6 KiB
Python
173 lines
7.6 KiB
Python
# Copyright 2017 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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"""Experimental API for building input pipelines.
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This module contains experimental `Dataset` sources and transformations that can
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be used in conjunction with the `tf.data.Dataset` API. Note that the
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`tf.data.experimental` API is not subject to the same backwards compatibility
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guarantees as `tf.data`, but we will provide deprecation advice in advance of
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removing existing functionality.
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See [Importing Data](https://tensorflow.org/guide/datasets) for an overview.
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@@AutoShardPolicy
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@@AutotuneAlgorithm
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@@AutotuneOptions
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@@CheckpointInputPipelineHook
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@@Counter
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@@CsvDataset
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@@DatasetInitializer
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@@DatasetStructure
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@@DistributeOptions
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@@ExternalStatePolicy
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@@OptimizationOptions
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@@Optional
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@@OptionalStructure
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@@RaggedTensorStructure
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@@RandomDataset
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@@Reducer
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@@SparseTensorStructure
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@@SqlDataset
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@@Structure
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@@TFRecordWriter
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@@TensorArrayStructure
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@@TensorStructure
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@@ThreadingOptions
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@@assert_cardinality
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@@at
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@@bucket_by_sequence_length
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@@cardinality
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@@choose_from_datasets
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@@copy_to_device
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@@dense_to_ragged_batch
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@@dense_to_sparse_batch
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@@distribute
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@@enable_debug_mode
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@@enumerate_dataset
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@@from_list
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@@from_variant
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@@get_next_as_optional
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@@get_single_element
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@@get_structure
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@@group_by_reducer
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@@group_by_window
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@@ignore_errors
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@@index_table_from_dataset
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@@load
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@@make_batched_features_dataset
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@@make_csv_dataset
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@@make_saveable_from_iterator
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@@map_and_batch
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@@map_and_batch_with_legacy_function
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@@parallel_interleave
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@@parse_example_dataset
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@@prefetch_to_device
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@@rejection_resample
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@@sample_from_datasets
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@@save
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@@scan
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@@shuffle_and_repeat
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@@snapshot
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@@table_from_dataset
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@@take_while
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@@to_variant
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@@unbatch
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@@unique
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@@AUTOTUNE
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@@INFINITE_CARDINALITY
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@@SHARD_HINT
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@@UNKNOWN_CARDINALITY
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"""
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# pylint: disable=unused-import
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from tensorflow.python.data.experimental import service
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from tensorflow.python.data.experimental.ops.batching import dense_to_ragged_batch
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from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch
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from tensorflow.python.data.experimental.ops.batching import map_and_batch
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from tensorflow.python.data.experimental.ops.batching import map_and_batch_with_legacy_function
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from tensorflow.python.data.experimental.ops.batching import unbatch
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from tensorflow.python.data.experimental.ops.cardinality import assert_cardinality
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from tensorflow.python.data.experimental.ops.cardinality import cardinality
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from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY
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from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY
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from tensorflow.python.data.experimental.ops.counter import Counter
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from tensorflow.python.data.experimental.ops.distribute import SHARD_HINT
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from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset
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from tensorflow.python.data.experimental.ops.error_ops import ignore_errors
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from tensorflow.python.data.experimental.ops.from_list import from_list
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from tensorflow.python.data.experimental.ops.get_single_element import get_single_element
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from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length
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from tensorflow.python.data.experimental.ops.grouping import group_by_reducer
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from tensorflow.python.data.experimental.ops.grouping import group_by_window
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from tensorflow.python.data.experimental.ops.grouping import Reducer
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from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets
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from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave
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from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets
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from tensorflow.python.data.experimental.ops.io import load
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from tensorflow.python.data.experimental.ops.io import save
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from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook
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from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator
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from tensorflow.python.data.experimental.ops.lookup_ops import DatasetInitializer
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from tensorflow.python.data.experimental.ops.lookup_ops import index_table_from_dataset
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from tensorflow.python.data.experimental.ops.lookup_ops import table_from_dataset
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from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset
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from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device
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from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device
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from tensorflow.python.data.experimental.ops.random_access import at
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from tensorflow.python.data.experimental.ops.random_ops import RandomDataset
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from tensorflow.python.data.experimental.ops.readers import CsvDataset
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from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset
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from tensorflow.python.data.experimental.ops.readers import make_csv_dataset
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from tensorflow.python.data.experimental.ops.readers import SqlDataset
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from tensorflow.python.data.experimental.ops.resampling import rejection_resample
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from tensorflow.python.data.experimental.ops.scan_ops import scan
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from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat
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from tensorflow.python.data.experimental.ops.snapshot import snapshot
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from tensorflow.python.data.experimental.ops.take_while_ops import take_while
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from tensorflow.python.data.experimental.ops.unique import unique
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from tensorflow.python.data.experimental.ops.writers import TFRecordWriter
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from tensorflow.python.data.ops.dataset_ops import AUTOTUNE
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from tensorflow.python.data.ops.dataset_ops import DatasetSpec as DatasetStructure
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from tensorflow.python.data.ops.dataset_ops import from_variant
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from tensorflow.python.data.ops.dataset_ops import get_structure
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from tensorflow.python.data.ops.dataset_ops import to_variant
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from tensorflow.python.data.ops.debug_mode import enable_debug_mode
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from tensorflow.python.data.ops.iterator_ops import get_next_as_optional
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from tensorflow.python.data.ops.optional_ops import Optional
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from tensorflow.python.data.ops.optional_ops import OptionalSpec as OptionalStructure
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from tensorflow.python.data.ops.options import AutoShardPolicy
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from tensorflow.python.data.ops.options import AutotuneAlgorithm
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from tensorflow.python.data.ops.options import AutotuneOptions
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from tensorflow.python.data.ops.options import DistributeOptions
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from tensorflow.python.data.ops.options import ExternalStatePolicy
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from tensorflow.python.data.ops.options import OptimizationOptions
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from tensorflow.python.data.ops.options import ThreadingOptions
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from tensorflow.python.data.util.structure import _RaggedTensorStructure as RaggedTensorStructure
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from tensorflow.python.data.util.structure import _SparseTensorStructure as SparseTensorStructure
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from tensorflow.python.data.util.structure import _TensorArrayStructure as TensorArrayStructure
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from tensorflow.python.data.util.structure import _TensorStructure as TensorStructure
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from tensorflow.python.framework.type_spec import TypeSpec as Structure
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# pylint: enable=unused-import
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from tensorflow.python.util.all_util import remove_undocumented
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_allowed_symbols = [
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"service",
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]
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remove_undocumented(__name__, _allowed_symbols)
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