59 lines
2.4 KiB
Python
59 lines
2.4 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.from_tensor_slices`."""
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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 tensor_shape
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from tensorflow.python.ops import gen_dataset_ops
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def _from_tensor_slices(tensors, name=None):
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return _TensorSliceDataset(tensors, name=name)
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class _TensorSliceDataset(dataset_ops.DatasetSource):
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"""A `Dataset` of slices from a dataset element."""
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def __init__(self, element, is_files=False, name=None):
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"""See `Dataset.from_tensor_slices` for details."""
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element = structure.normalize_element(element)
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batched_spec = structure.type_spec_from_value(element)
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self._tensors = structure.to_batched_tensor_list(batched_spec, element)
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if not self._tensors:
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raise ValueError("Invalid `element`. `element` should not be empty.")
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self._structure = nest.map_structure(
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lambda component_spec: component_spec._unbatch(), batched_spec) # pylint: disable=protected-access
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self._name = name
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batch_dim = tensor_shape.Dimension(
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tensor_shape.dimension_value(self._tensors[0].get_shape()[0]))
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for t in self._tensors[1:]:
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batch_dim.assert_is_compatible_with(
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tensor_shape.Dimension(
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tensor_shape.dimension_value(t.get_shape()[0])))
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variant_tensor = gen_dataset_ops.tensor_slice_dataset(
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self._tensors,
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output_shapes=structure.get_flat_tensor_shapes(self._structure),
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is_files=is_files,
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metadata=self._metadata.SerializeToString())
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super().__init__(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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