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# Copyright 2017 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.
# ==============================================================================
"""Python dataset sparse tensor utility functitons."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.data.util import nest
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import sparse_ops
def any_sparse(classes):
"""Checks for sparse tensor.
Args:
classes: a structure of objects that identify the dataset item classes
Returns:
`True` if `classes` contains a sparse tensor type and `False` otherwise.
"""
return any([c is sparse_tensor.SparseTensor for c in nest.flatten(classes)])
def as_dense_shapes(shapes, classes):
"""Converts sparse tensor shapes to their physical shapes.
Args:
shapes: a structure of shapes to convert.
classes: a structure of objects that identify the dataset item classes
Returns:
a structure matching the nested structure of `shapes`, containing
`tensor_shape.unknown_shape()` at positions where `classes` contains
`tf.SparseTensor` and matching contents of `shapes` otherwise
"""
ret = nest.pack_sequence_as(shapes, [
tensor_shape.unknown_shape() if c is sparse_tensor.SparseTensor else shape
for shape, c in zip(nest.flatten(shapes), nest.flatten(classes))
])
return ret
def as_dense_types(types, classes):
"""Converts sparse tensor types to `dtypes.variant`.
Args:
types: a structure of types to convert.
classes: a structure of objects that identify the dataset item classes
Returns:
a structure matching the nested structure of `types`, containing
`dtypes.variant` at positions where `classes` contains `tf.SparseTensor` and
matching contents of `types` otherwise
"""
ret = nest.pack_sequence_as(types, [
dtypes.variant if c is sparse_tensor.SparseTensor else ty
for ty, c in zip(nest.flatten(types), nest.flatten(classes))
])
return ret
def deserialize_sparse_tensors(tensors, types, shapes, classes):
"""Deserializes sparse tensors.
Args:
tensors: a structure of tensors to deserialize.
types: a structure that holds information about types of `tensors`
shapes: a structure that holds information about shapes of `tensors`
classes: a structure of objects that identify the dataset item classes
Returns:
`tensors` with any serialized sparse tensors replaced by their deserialized
version.
"""
ret = nest.pack_sequence_as(types, [
sparse_ops.deserialize_sparse(tensor, dtype=ty, rank=shape.ndims)
if c is sparse_tensor.SparseTensor else tensor
for (tensor, ty, shape, c) in zip(
nest.flatten(tensors), nest.flatten(types), nest.flatten(shapes),
nest.flatten(classes))
])
return ret
def get_classes(tensors):
"""Gets classes for a structure of tensors.
Args:
tensors: the tensor structure to get classes for.
Returns:
a structure matching the nested structure of `tensors`, containing
`tf.SparseTensor` at positions where `tensors` contains a sparse tensor and
`tf.Tensor` otherwise
"""
return nest.pack_sequence_as(tensors, [
sparse_tensor.SparseTensor
if isinstance(tensor, sparse_tensor.SparseTensor) else ops.Tensor
for tensor in nest.flatten(tensors)
])
def serialize_many_sparse_tensors(tensors):
"""Serializes many sparse tensors into a batch.
Args:
tensors: a tensor structure to serialize.
Returns:
`tensors` with any sparse tensors replaced by the serialized batch.
"""
ret = nest.pack_sequence_as(tensors, [
sparse_ops.serialize_many_sparse(tensor, out_type=dtypes.variant)
if sparse_tensor.is_sparse(tensor) else tensor
for tensor in nest.flatten(tensors)
])
return ret
def serialize_sparse_tensors(tensors):
"""Serializes sparse tensors.
Args:
tensors: a tensor structure to serialize.
Returns:
`tensors` with any sparse tensors replaced by the their serialized version.
"""
ret = nest.pack_sequence_as(tensors, [
sparse_ops.serialize_sparse(tensor, out_type=dtypes.variant)
if isinstance(tensor, sparse_tensor.SparseTensor) else tensor
for tensor in nest.flatten(tensors)
])
return ret
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