108 lines
4.5 KiB
Python
108 lines
4.5 KiB
Python
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# Copyright 2017 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.ragged_batch`."""
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.data.util import nest
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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.framework import tensor_spec
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from tensorflow.python.ops.ragged import ragged_tensor
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def _ragged_batch(input_dataset,
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batch_size,
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drop_remainder=False,
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row_splits_dtype=dtypes.int64,
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name=None):
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ragged_dataset = _DenseToRaggedDataset(input_dataset, row_splits_dtype, name)
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return ragged_dataset.batch(batch_size, drop_remainder)
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class _DenseToRaggedDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that encodes dense inputs as ragged (w/ ragged_rank=0).
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In particular:
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* Any tf.Tensor elements with rank>0 are encoded as ragged tensors with
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ragged_rank=0. This allows tensors with varying shape to be batched
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together.
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* Any other elements are left as-is.
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"""
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def __init__(self, input_dataset, row_splits_dtype, name=None):
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"""Constructs a new _DenseToRaggedDataset.
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Args:
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input_dataset: The dataset whose tf.Tensor elements should be made ragged.
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row_splits_dtype: The dtype that should be used for the `row_splits` of
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any new ragged tensors. Existing `tf.RaggedTensor` elements do *not*
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have their row_splits dtype changed.
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name: (Optional.) A string indicating a name for the `tf.data` operation.
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"""
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# Replace each TensorSpec in the input dataset's structure with a
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# corresponding RaggedTensorSpec.
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def to_ragged_spec(spec):
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"""Returns the new spec based on RaggedTensors."""
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if (not isinstance(spec, tensor_spec.TensorSpec) or
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spec.shape.rank is None or
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spec.shape.is_fully_defined()):
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return spec
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else:
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ragged_rank = max([
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axis for (axis, size) in enumerate(spec.shape.as_list())
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if size is None
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])
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return ragged_tensor.RaggedTensorSpec(
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shape=spec.shape,
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dtype=spec.dtype,
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ragged_rank=ragged_rank,
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row_splits_dtype=row_splits_dtype)
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self._structure = nest.map_structure(to_ragged_spec,
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input_dataset.element_spec)
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# Replace each tf.Tensor value in the input dataset with a variant-encoded
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# RaggedTensor. Since we're updating the corresponding structure to be
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# a RaggedTensorSpec, this variant-encoded tensor will be decoded with
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# RaggedTensorSpec._from_tensor_list.
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def to_ragged_variant(value):
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"""Re-encode Tensors as RaggedTensors."""
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if (not isinstance(value, ops.Tensor) or
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value.shape.rank is None or
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value.shape.is_fully_defined()):
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return value
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else:
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spec = to_ragged_spec(tensor_spec.TensorSpec.from_tensor(value))
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if spec._ragged_rank > 0: # pylint: disable=protected-access
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value = ragged_tensor.RaggedTensor.from_tensor(
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value, ragged_rank=spec._ragged_rank) # pylint: disable=protected-access
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return spec._to_tensor_list(value)[0] # pylint: disable=protected-access
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# Tuples are automatically unpacked by `dataset.map` so we repack them.
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if structured_function._should_unpack(input_dataset.element_spec): # pylint: disable=protected-access
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map_fn = lambda *value: nest.map_structure(to_ragged_variant, value)
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else:
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map_fn = lambda value: nest.map_structure(to_ragged_variant, value)
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self._mapped_dataset = input_dataset.map(map_fn)
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self._name = name
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variant = self._mapped_dataset._variant_tensor # pylint: disable=protected-access
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super().__init__(input_dataset, variant)
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@property
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def element_spec(self):
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return self._structure
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