273 lines
11 KiB
Python
273 lines
11 KiB
Python
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# 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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import functools
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import numpy as np
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from tensorflow.python.data.experimental.ops import random_access
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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 array_ops
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from tensorflow.python.ops import gen_dataset_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import stateless_random_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, "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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def _process_file_infos(file_infos):
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"""Computes aggregate information about files to read.
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The method collects information about the files to read, the total number of
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elements, and arrays that can be used to account for elements to be skipped,
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which can be specified via the "skip" and "take" keys.
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To account for elements to skip, the range of each file can be divided into
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three regions:
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- S (elements to skip)
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- T (elements to read)
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- R (remainder of elements that will also be skipped)
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The `thresholds` and `offsets` arrays are initialized as follows:
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`thresholds = [0, T_1, T_1 + T_2, ...]` and
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`offsets = [S_1, S_1 + R_1 + S_2, S_1 + R_1 + S_2 + R_2 + S_3, ...]`
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This makes it possible to map an index from a contiguous range
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`(0...num_elements_to_read)` to an index in the range of all elements,
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skipping over elements as per the "skip" and "take" keys values. In
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particular, for a given input index `X`, we find the greatest `thresholds`
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value that is smaller or equal to `X`. Let `t(X)` denotes such index in the
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`thresholds` array. The output index is computed as `X + offsets[t(X)]`.
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Args:
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file_infos: See `file_infos` argument of `index_shuffle` for details.
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Returns:
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A dictionary containing the following keys:
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- `files`, the vector of pathnames of files to read
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- `num_elements`, an integer identifying the total number of elements
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- `offsets`, the vector of offsets to use for index adjustment (in case
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any elements should be skipped)
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- `thresholds`, the vector of thresholds to use for index adjustment (in
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case any elements should be skipped)
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"""
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files = []
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num_elements = 0
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offsets = np.int64([])
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offset_sum = 0
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thresholds = np.int64([])
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threshold_sum = 0
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adjustment_needed = False
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for file_info in file_infos:
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files.append(file_info["path"])
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skip = 0
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if "skip" in file_info:
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if file_info["skip"] < -1:
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raise ValueError("`skip` should be greater than `-1` but got {}".format(
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file_info["skip"]))
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if file_info["skip"] == -1:
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skip = file_info["num_elements"]
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else:
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skip = min(file_info["skip"], file_info["num_elements"])
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take = file_info["num_elements"] - skip
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if "take" in file_info:
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if file_info["take"] < -1:
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raise ValueError("`take` should be greater than `-1` but got {}".format(
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file_info["take"]))
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# `file_info["take"] == -1` is a no-op
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if file_info["take"] != -1:
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take = min(file_info["take"], take)
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remainder = file_info["num_elements"] - skip - take
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if take != file_info["num_elements"]:
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adjustment_needed = True
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num_elements += take
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offsets = np.append(offsets, offset_sum + skip)
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offset_sum += skip + remainder
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thresholds = np.append(thresholds, threshold_sum)
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threshold_sum += take
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result = {"files": files, "num_elements": num_elements}
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if adjustment_needed:
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result["offsets"] = offsets
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result["thresholds"] = thresholds
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return result
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def _adjust_index(index, thresholds, offsets):
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"""Adjusts index to account for elements to be skipped."""
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t_index = array_ops.shape(
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array_ops.boolean_mask(
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thresholds,
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math_ops.less_equal(thresholds, index)))[0] - 1
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return index + array_ops.gather(offsets, t_index)
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# TODO(jsimsa): Expose this method in the public API. When we do, consider
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# defining `FileInfo` as a public API to encapsulate the information provided
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# through the `file_infos` argument.
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def index_shuffle(file_infos,
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reader_factory,
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seed=None,
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reshuffle_each_iteration=False,
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num_parallel_calls=dataset_ops.AUTOTUNE):
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"""Creates a (globally) shuffled dataset from the given set of files.
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Unlike `tf.data.Dataset.shuffle()`, which uses an in-memory buffer to shuffle
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elements of input dataset in a streaming fashion,
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`tf.data.experimental.index_shuffle()` performs a global shuffle of element
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indices and then reads the data in a shuffled order. The advantage of
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`index_shuffle()` is that it can perform global shuffle of datasets that do
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not fit into memory (as long as the array of their indices does) and that the
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shuffling logic it provides is compatible with symbolic checkpointing. The
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disadvantage of `index_shuffle()` is that reading data in a shuffled random
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order will in general not be as efficient as reading data sequentially.
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Args:
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file_infos: A list of dictionaries that describe each file of the input
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dataset. Each dictionary is expected to contain the "path" key, which
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identifies the path of the file and the "num_elements" key, which
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identifies the number of elements in the file. In addition, the "skip"
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and "take" keys can be used to identify the number of elements to skip
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and take respectively. By default, no elements are skipped and all
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elements are taken.
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reader_factory: A function that maps a sequence of filenames to an instance
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of `tf.data.Dataset` that reads data from the files.
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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 shuffle the order of elements. Default to
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non-deterministic seed.
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reshuffle_each_iteration: (Optional.) A `tf.bool` scalar `tf.Tensor`, that
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determines whether to change the shuffle order each iteration. Defaults to
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`False`.
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num_parallel_calls: (Optional.) A `tf.int64` scalar `tf.Tensor`, that
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determines the maximum number of random access operations to perform
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in parallel. By default, the tf.data runtime uses autotuning to determine
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the value dynamically.
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Returns:
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A `tf.data.Dataset` object, representing a globally shuffled dataset of
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the input data.
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"""
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result = _process_file_infos(file_infos)
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def sequential_index_shuffle(seeds):
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dataset = dataset_ops.Dataset.range(result["num_elements"])
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def read_element(dataset, index):
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# 1) Shuffle the index.
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shuffled_index = stateless_random_ops.index_shuffle(
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index, seeds, result["num_elements"] - 1)
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# 2) If needed, adjust the index to the non-contiguous range.
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if "thresholds" in result and "offsets" in result:
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shuffled_index = _adjust_index(shuffled_index, result["thresholds"],
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result["offsets"])
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# 3) Perform the read.
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return random_access.at(dataset, shuffled_index)
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# We evaluate `reader_factory()` eagerly to prevent the dataset from being
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# created on every lookup.
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map_func = functools.partial(read_element, reader_factory(result["files"]))
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return dataset.map(map_func, num_parallel_calls=num_parallel_calls)
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rng_ds = dataset_ops.Dataset.random(
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seed=seed,
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rerandomize_each_iteration=reshuffle_each_iteration)
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rng_ds = rng_ds.take(2).batch(2, drop_remainder=True)
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return rng_ds.flat_map(sequential_index_shuffle)
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