110 lines
4.5 KiB
Python
110 lines
4.5 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 shuffle ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.util import random_seed
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from tensorflow.python.framework import constant_op
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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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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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class _ShuffleAndRepeatDataset(dataset_ops.UnaryUnchangedStructureDataset):
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"""A `Dataset` that fuses `shuffle` and `repeat`."""
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def __init__(self, input_dataset, buffer_size, count=None, seed=None):
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self._input_dataset = input_dataset
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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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if count is None:
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self._count = constant_op.constant(-1, dtype=dtypes.int64, name="count")
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else:
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self._count = ops.convert_to_tensor(
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count, dtype=dtypes.int64, name="count")
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self._seed, self._seed2 = random_seed.get_seed(seed)
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variant_tensor = gen_dataset_ops.shuffle_and_repeat_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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buffer_size=self._buffer_size,
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count=self._count,
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seed=self._seed,
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seed2=self._seed2,
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**self._flat_structure)
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super(_ShuffleAndRepeatDataset, self).__init__(input_dataset,
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variant_tensor)
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@deprecation.deprecated(
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None,
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"Use `tf.data.Dataset.shuffle(buffer_size, seed)` followed by "
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"`tf.data.Dataset.repeat(count)`. Static tf.data optimizations will take "
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"care of using the fused implementation.")
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@tf_export("data.experimental.shuffle_and_repeat")
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def shuffle_and_repeat(buffer_size, count=None, seed=None):
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"""Shuffles and repeats a Dataset, reshuffling with each repetition.
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>>> d = tf.data.Dataset.from_tensor_slices([1, 2, 3])
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>>> d = d.apply(tf.data.experimental.shuffle_and_repeat(2, count=2))
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>>> [elem.numpy() for elem in d] # doctest: +SKIP
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[2, 3, 1, 1, 3, 2]
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```python
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dataset.apply(
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tf.data.experimental.shuffle_and_repeat(buffer_size, count, seed))
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```
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produces the same output as
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```python
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dataset.shuffle(
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buffer_size, seed=seed, reshuffle_each_iteration=True).repeat(count)
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```
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In each repetition, this dataset fills a buffer with `buffer_size` elements,
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then randomly samples elements from this buffer, replacing the selected
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elements with new elements. For perfect shuffling, set the buffer size equal
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to the full size of the dataset.
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For instance, if your dataset contains 10,000 elements but `buffer_size` is
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set to 1,000, then `shuffle` will initially select a random element from
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only the first 1,000 elements in the buffer. Once an element is selected,
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its space in the buffer is replaced by the next (i.e. 1,001-st) element,
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maintaining the 1,000 element buffer.
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Args:
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buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the maximum
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number elements that will be buffered when prefetching.
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count: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the number
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of times the dataset should be repeated. The default behavior (if `count`
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is `None` or `-1`) is for the dataset be repeated indefinitely.
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seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random
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seed that will be used to create the distribution. See
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`tf.random.set_seed` for behavior.
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Returns:
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A `Dataset` transformation function, which can be passed to
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`tf.data.Dataset.apply`.
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"""
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def _apply_fn(dataset): # pylint: disable=missing-docstring
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return _ShuffleAndRepeatDataset(dataset, buffer_size, count, seed)
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return _apply_fn
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