77 lines
3.1 KiB
Python
77 lines
3.1 KiB
Python
# Copyright 2022 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.
|
|
# ==============================================================================
|
|
"""The implementation of `tf.data.Dataset.window`."""
|
|
|
|
from tensorflow.python.data.ops import dataset_ops
|
|
from tensorflow.python.data.util import nest
|
|
from tensorflow.python.data.util import structure
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.ops import gen_dataset_ops
|
|
|
|
|
|
def _window(input_dataset, size, shift, stride, drop_remainder, name):
|
|
if shift is None:
|
|
shift = size
|
|
return _WindowDataset(
|
|
input_dataset, size, shift, stride, drop_remainder, name=name)
|
|
|
|
|
|
class _WindowDataset(dataset_ops.UnaryDataset):
|
|
"""A dataset that creates window datasets from the input elements."""
|
|
|
|
def __init__(self,
|
|
input_dataset,
|
|
size,
|
|
shift,
|
|
stride,
|
|
drop_remainder,
|
|
name=None):
|
|
"""See `window()` for more details."""
|
|
self._input_dataset = input_dataset
|
|
self._size = ops.convert_to_tensor(size, dtype=dtypes.int64, name="size")
|
|
self._shift = ops.convert_to_tensor(shift, dtype=dtypes.int64, name="shift")
|
|
self._stride = ops.convert_to_tensor(
|
|
stride, dtype=dtypes.int64, name="stride")
|
|
self._drop_remainder = ops.convert_to_tensor(
|
|
drop_remainder, dtype=dtypes.bool, name="drop_remainder")
|
|
self._structure = nest.pack_sequence_as(
|
|
dataset_ops.get_legacy_output_classes(input_dataset),
|
|
[
|
|
dataset_ops.DatasetSpec( # pylint: disable=g-complex-comprehension
|
|
structure.convert_legacy_structure(output_type, output_shape,
|
|
output_class))
|
|
for output_class, output_shape, output_type in zip(
|
|
nest.flatten(
|
|
dataset_ops.get_legacy_output_classes(input_dataset)),
|
|
nest.flatten(
|
|
dataset_ops.get_legacy_output_shapes(input_dataset)),
|
|
nest.flatten(
|
|
dataset_ops.get_legacy_output_types(input_dataset)))
|
|
])
|
|
self._name = name
|
|
variant_tensor = gen_dataset_ops.window_dataset(
|
|
input_dataset._variant_tensor, # pylint: disable=protected-access
|
|
size=self._size,
|
|
shift=self._shift,
|
|
stride=self._stride,
|
|
drop_remainder=self._drop_remainder,
|
|
**self._common_args)
|
|
super().__init__(input_dataset, variant_tensor)
|
|
|
|
@property
|
|
def element_spec(self):
|
|
return self._structure
|