52 lines
2.2 KiB
Python
52 lines
2.2 KiB
Python
# 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.prefetch`."""
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import debug_mode
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import gen_dataset_ops
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def _prefetch(input_dataset, buffer_size, name=None): # pylint: disable=unused-private-name
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"""See `Dataset.prefetch()` for details."""
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if debug_mode.DEBUG_MODE:
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return input_dataset
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return _PrefetchDataset(input_dataset, buffer_size, name=name)
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class _PrefetchDataset(dataset_ops.UnaryUnchangedStructureDataset):
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"""A `Dataset` that asynchronously prefetches its input."""
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def __init__(self, input_dataset, buffer_size, slack_period=None, name=None):
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"""See `Dataset.prefetch()` for details."""
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self._input_dataset = input_dataset
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if buffer_size is None:
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buffer_size = dataset_ops.AUTOTUNE
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self._buffer_size = ops.convert_to_tensor(
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buffer_size, dtype=dtypes.int64, name="buffer_size")
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self._name = name
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# pylint: disable=protected-access
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# We colocate the prefetch dataset with its input as this collocation only
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# happens automatically in graph mode.
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with ops.colocate_with(input_dataset._variant_tensor):
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variant_tensor = gen_dataset_ops.prefetch_dataset(
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input_dataset._variant_tensor,
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buffer_size=self._buffer_size,
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slack_period=slack_period,
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**self._common_args)
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super().__init__(input_dataset, variant_tensor)
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