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