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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Sliding dataset transformations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import gen_dataset_ops
from tensorflow.python.util import deprecation
class _SlideDataset(dataset_ops.Dataset):
"""A `Dataset` that passes a sliding window over its input."""
def __init__(self, input_dataset, window_size, window_shift, window_stride):
"""See `sliding_window_batch` for details."""
super(_SlideDataset, self).__init__()
self._input_dataset = input_dataset
self._window_size = ops.convert_to_tensor(
window_size, dtype=dtypes.int64, name="window_stride")
self._window_stride = ops.convert_to_tensor(
window_stride, dtype=dtypes.int64, name="window_stride")
self._window_shift = ops.convert_to_tensor(
window_shift, dtype=dtypes.int64, name="window_shift")
def _as_variant_tensor(self):
return gen_dataset_ops.slide_dataset(
self._input_dataset._as_variant_tensor(), # pylint: disable=protected-access
window_size=self._window_size,
window_shift=self._window_shift,
window_stride=self._window_stride,
**dataset_ops.flat_structure(self))
@property
def output_classes(self):
return self._input_dataset.output_classes
@property
def output_shapes(self):
input_shapes = self._input_dataset.output_shapes
return nest.pack_sequence_as(input_shapes, [
tensor_shape.vector(None).concatenate(s)
for s in nest.flatten(self._input_dataset.output_shapes)
])
@property
def output_types(self):
return self._input_dataset.output_types
@deprecation.deprecated_args(
None, "stride is deprecated, use window_shift instead", "stride")
def sliding_window_batch(window_size,
stride=None,
window_shift=None,
window_stride=1):
"""A sliding window over a dataset.
This transformation passes a sliding window over this dataset. The window size
is `window_size`, the stride of the input elements is `window_stride`, and the
shift between consecutive windows is `window_shift`. If the remaining elements
cannot fill up the sliding window, this transformation will drop the final
smaller element. For example:
```python
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { [1], [2], [3], [4], [5], [6] }
a.apply(sliding_window_batch(window_size=3)) ==
{ [[1], [2], [3]], [[2], [3], [4]], [[3], [4], [5]], [[4], [5], [6]] }
a.apply(sliding_window_batch(window_size=3, window_shift=2)) ==
{ [[1], [2], [3]], [[3], [4], [5]] }
a.apply(sliding_window_batch(window_size=3, window_stride=2)) ==
{ [[1], [3], [5]], [[2], [4], [6]] }
```
Args:
window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
elements in the sliding window. It must be positive.
stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
forward shift of the sliding window in each iteration. The default is `1`.
It must be positive. Deprecated alias for `window_shift`.
window_shift: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
forward shift of the sliding window in each iteration. The default is `1`.
It must be positive.
window_stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
stride of the input elements in the sliding window. The default is `1`.
It must be positive.
Returns:
A `Dataset` transformation function, which can be passed to
@{tf.data.Dataset.apply}.
Raises:
ValueError: if invalid arguments are provided.
"""
if stride is None and window_shift is None:
window_shift = 1
elif stride is not None and window_shift is None:
window_shift = stride
elif stride is not None and window_shift is not None:
raise ValueError("Cannot specify both `stride` and `window_shift`")
def _apply_fn(dataset):
return _SlideDataset(dataset, window_size, window_shift, window_stride)
return _apply_fn
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