59 lines
2.5 KiB
Python
59 lines
2.5 KiB
Python
# 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.unbatch`."""
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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.framework import tensor_shape
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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def _unbatch(input_dataset, name=None): # pylint: disable=unused-private-name
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"""See `Dataset.unbatch()` for details."""
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normalized_dataset = dataset_ops.normalize_to_dense(input_dataset)
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return _UnbatchDataset(normalized_dataset, name=name)
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class _UnbatchDataset(dataset_ops.UnaryDataset):
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"""A dataset that splits the elements of its input into multiple elements."""
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def __init__(self, input_dataset, name=None):
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"""See `unbatch()` for more details."""
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flat_shapes = input_dataset._flat_shapes # pylint: disable=protected-access
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if any(s.ndims == 0 for s in flat_shapes):
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raise ValueError("Cannot unbatch an input with scalar components.")
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known_batch_dim = tensor_shape.Dimension(None)
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for s in flat_shapes:
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try:
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known_batch_dim = known_batch_dim.merge_with(s[0])
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except ValueError as e:
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raise ValueError(
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f"`unbatch()` is only supported for datasets of elements whose "
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f"components have a matching leading dimension. Encountered both "
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f"{known_batch_dim} and {s[0]}.") from e
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self._input_dataset = input_dataset
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self._structure = nest.map_structure(
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lambda component_spec: component_spec._unbatch(), # pylint: disable=protected-access
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dataset_ops.get_structure(input_dataset))
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self._name = name
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variant_tensor = ged_ops.unbatch_dataset(
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self._input_dataset._variant_tensor, # pylint: disable=protected-access
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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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