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# Copyright 2018 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.
# ==============================================================================
"""An Optional type for representing potentially missing values."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
from tensorflow.python.data.util import nest
from tensorflow.python.data.util import sparse
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import gen_dataset_ops
class Optional(object):
"""Wraps a nested structure of tensors that may/may not be present at runtime.
An `Optional` can represent the result of an operation that may fail as a
value, rather than raising an exception and halting execution. For example,
@{tf.contrib.data.get_next_as_optional} returns an `Optional` that either
contains the next value from a @{tf.data.Iterator} if one exists, or a "none"
value that indicates the end of the sequence has been reached.
"""
@abc.abstractmethod
def has_value(self, name=None):
"""Returns a tensor that evaluates to `True` if this optional has a value.
Args:
name: (Optional.) A name for the created operation.
Returns:
A scalar `tf.Tensor` of type `tf.bool`.
"""
raise NotImplementedError("Optional.has_value()")
@abc.abstractmethod
def get_value(self, name=None):
"""Returns a nested structure of values wrapped by this optional.
If this optional does not have a value (i.e. `self.has_value()` evaluates
to `False`), this operation will raise @{tf.errors.InvalidArgumentError}
at runtime.
Args:
name: (Optional.) A name for the created operation.
Returns:
A nested structure of `tf.Tensor` and/or `tf.SparseTensor` objects.
"""
raise NotImplementedError("Optional.get_value()")
@abc.abstractproperty
def output_classes(self):
"""Returns the class of each component of this optional.
The expected values are `tf.Tensor` and `tf.SparseTensor`.
Returns:
A nested structure of Python `type` objects corresponding to each
component of this optional.
"""
raise NotImplementedError("Optional.output_classes")
@abc.abstractproperty
def output_shapes(self):
"""Returns the shape of each component of this optional.
Returns:
A nested structure of `tf.TensorShape` objects corresponding to each
component of this optional.
"""
raise NotImplementedError("Optional.output_shapes")
@abc.abstractproperty
def output_types(self):
"""Returns the type of each component of this optional.
Returns:
A nested structure of `tf.DType` objects corresponding to each component
of this optional.
"""
raise NotImplementedError("Optional.output_types")
@staticmethod
def from_value(value):
"""Returns an `Optional` that wraps the given value.
Args:
value: A nested structure of `tf.Tensor` and/or `tf.SparseTensor` objects.
Returns:
An `Optional` that wraps `value`.
"""
# TODO(b/110122868): Consolidate this destructuring logic with the
# similar code in `Dataset.from_tensors()`.
with ops.name_scope("optional") as scope:
with ops.name_scope("value"):
value = nest.pack_sequence_as(value, [
sparse_tensor_lib.SparseTensor.from_value(t)
if sparse_tensor_lib.is_sparse(t) else ops.convert_to_tensor(
t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(value))
])
encoded_value = nest.flatten(sparse.serialize_sparse_tensors(value))
output_classes = sparse.get_classes(value)
output_shapes = nest.pack_sequence_as(
value, [t.get_shape() for t in nest.flatten(value)])
output_types = nest.pack_sequence_as(
value, [t.dtype for t in nest.flatten(value)])
return _OptionalImpl(
gen_dataset_ops.optional_from_value(encoded_value, name=scope),
output_shapes, output_types, output_classes)
@staticmethod
def none_from_structure(output_shapes, output_types, output_classes):
"""Returns an `Optional` that has no value.
NOTE: This method takes arguments that define the structure of the value
that would be contained in the returned `Optional` if it had a value.
Args:
output_shapes: A nested structure of `tf.TensorShape` objects
corresponding to each component of this optional.
output_types: A nested structure of `tf.DType` objects corresponding to
each component of this optional.
output_classes: A nested structure of Python `type` objects corresponding
to each component of this optional.
Returns:
An `Optional` that has no value.
"""
return _OptionalImpl(gen_dataset_ops.optional_none(), output_shapes,
output_types, output_classes)
class _OptionalImpl(Optional):
"""Concrete implementation of `tf.contrib.data.Optional`.
NOTE(mrry): This implementation is kept private, to avoid defining
`Optional.__init__()` in the public API.
"""
def __init__(self, variant_tensor, output_shapes, output_types,
output_classes):
# TODO(b/110122868): Consolidate the structure validation logic with the
# similar logic in `Iterator.from_structure()` and
# `Dataset.from_generator()`.
output_types = nest.map_structure(dtypes.as_dtype, output_types)
output_shapes = nest.map_structure_up_to(
output_types, tensor_shape.as_shape, output_shapes)
nest.assert_same_structure(output_types, output_shapes)
nest.assert_same_structure(output_types, output_classes)
self._variant_tensor = variant_tensor
self._output_shapes = output_shapes
self._output_types = output_types
self._output_classes = output_classes
def has_value(self, name=None):
return gen_dataset_ops.optional_has_value(self._variant_tensor, name=name)
def get_value(self, name=None):
# TODO(b/110122868): Consolidate the restructuring logic with similar logic
# in `Iterator.get_next()` and `StructuredFunctionWrapper`.
with ops.name_scope(name, "OptionalGetValue",
[self._variant_tensor]) as scope:
return sparse.deserialize_sparse_tensors(
nest.pack_sequence_as(
self._output_types,
gen_dataset_ops.optional_get_value(
self._variant_tensor,
name=scope,
output_types=nest.flatten(
sparse.as_dense_types(self._output_types,
self._output_classes)),
output_shapes=nest.flatten(
sparse.as_dense_shapes(self._output_shapes,
self._output_classes)))),
self._output_types, self._output_shapes, self._output_classes)
@property
def output_classes(self):
return self._output_classes
@property
def output_shapes(self):
return self._output_shapes
@property
def output_types(self):
return self._output_types
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