# Copyright 2015 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.
# ==============================================================================
"""Sparse tensors."""
# pylint: disable=g-bad-name
import collections
import numpy as np
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
from tensorflow.python import tf2
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import type_spec
from tensorflow.python.ops import gen_sparse_ops
from tensorflow.python.types import internal
from tensorflow.python.util import _pywrap_utils
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=protected-access
_eval_using_default_session = ops._eval_using_default_session
_override_helper = ops._override_helper
# pylint: enable=protected-access
[文档]@tf_export("sparse.SparseTensor", "SparseTensor")
class SparseTensor(internal.NativeObject, composite_tensor.CompositeTensor):
"""Represents a sparse tensor.
TensorFlow represents a sparse tensor as three separate dense tensors:
`indices`, `values`, and `dense_shape`. In Python, the three tensors are
collected into a `SparseTensor` class for ease of use. If you have separate
`indices`, `values`, and `dense_shape` tensors, wrap them in a `SparseTensor`
object before passing to the ops below.
Concretely, the sparse tensor `SparseTensor(indices, values, dense_shape)`
comprises the following components, where `N` and `ndims` are the number
of values and number of dimensions in the `SparseTensor`, respectively:
* `indices`: A 2-D int64 tensor of shape `[N, ndims]`, which specifies the
indices of the elements in the sparse tensor that contain nonzero values
(elements are zero-indexed). For example, `indices=[[1,3], [2,4]]` specifies
that the elements with indexes of [1,3] and [2,4] have nonzero values.
* `values`: A 1-D tensor of any type and shape `[N]`, which supplies the
values for each element in `indices`. For example, given `indices=[[1,3],
[2,4]]`, the parameter `values=[18, 3.6]` specifies that element [1,3] of
the sparse tensor has a value of 18, and element [2,4] of the tensor has a
value of 3.6.
* `dense_shape`: A 1-D int64 tensor of shape `[ndims]`, which specifies the
dense_shape of the sparse tensor. Takes a list indicating the number of
elements in each dimension. For example, `dense_shape=[3,6]` specifies a
two-dimensional 3x6 tensor, `dense_shape=[2,3,4]` specifies a
three-dimensional 2x3x4 tensor, and `dense_shape=[9]` specifies a
one-dimensional tensor with 9 elements.
The corresponding dense tensor satisfies:
```python
dense.shape = dense_shape
dense[tuple(indices[i])] = values[i]
```
By convention, `indices` should be sorted in row-major order (or equivalently
lexicographic order on the tuples `indices[i]`). This is not enforced when
`SparseTensor` objects are constructed, but most ops assume correct ordering.
If the ordering of sparse tensor `st` is wrong, a fixed version can be
obtained by calling `tf.sparse.reorder(st)`.
Example: The sparse tensor
```python
SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
```
represents the dense tensor
```python
[[1, 0, 0, 0]
[0, 0, 2, 0]
[0, 0, 0, 0]]
```
"""
[文档] @classmethod
def from_value(cls, sparse_tensor_value):
if not is_sparse(sparse_tensor_value):
raise TypeError(f"Argument sparse_tensor_value={sparse_tensor_value} "
"is neither a SparseTensor nor SparseTensorValue.")
return SparseTensor(
indices=sparse_tensor_value.indices,
values=sparse_tensor_value.values,
dense_shape=sparse_tensor_value.dense_shape)
def __init__(self, indices, values, dense_shape):
"""Creates a `SparseTensor`.
Args:
indices: A 2-D int64 tensor of shape `[N, ndims]`.
values: A 1-D tensor of any type and shape `[N]`.
dense_shape: A 1-D int64 tensor of shape `[ndims]`.
Raises:
ValueError: When building an eager SparseTensor if `dense_shape` is
unknown or contains unknown elements (None or -1).
"""
with ops.name_scope(None, "SparseTensor", [indices, values, dense_shape]):
indices = ops.convert_to_tensor(
indices, name="indices", dtype=dtypes.int64)
# TODO(touts): Consider adding mutable_values() when 'values'
# is a VariableOp and updating users of SparseTensor.
values = ops.convert_to_tensor(values, name="values")
dense_shape = ops.convert_to_tensor(
dense_shape, name="dense_shape", dtype=dtypes.int64)
dense_shape_default = tensor_util.constant_value_as_shape(dense_shape)
self._indices = indices
self._values = values
self._dense_shape = dense_shape
self._dense_shape_default = dense_shape_default
indices_shape = indices.shape.with_rank(2)
values_shape = values.shape.with_rank(1)
dense_shape_shape = dense_shape.shape.with_rank(1)
# Assert number of rows in indices match the number of elements in values.
indices_shape.dims[0].assert_is_compatible_with(values_shape.dims[0])
# Assert number of columns in indices matches the number of elements in
# dense_shape.
indices_shape.dims[1].assert_is_compatible_with(dense_shape_shape.dims[0])
[文档] def get_shape(self):
"""Get the `TensorShape` representing the shape of the dense tensor.
Returns:
A `TensorShape` object.
"""
return self._dense_shape_default
@property
def indices(self):
"""The indices of non-zero values in the represented dense tensor.
Returns:
A 2-D Tensor of int64 with dense_shape `[N, ndims]`, where `N` is the
number of non-zero values in the tensor, and `ndims` is the rank.
"""
return self._indices
@property
def values(self):
"""The non-zero values in the represented dense tensor.
Returns:
A 1-D Tensor of any data type.
"""
return self._values
[文档] def with_values(self, new_values):
"""Returns a copy of `self` with `values` replaced by `new_values`.
This method produces a new `SparseTensor` that has the same nonzero
`indices` and same `dense_shape`, but updated values.
Args:
new_values: The values of the new `SparseTensor`. Needs to have the same
shape as the current `.values` `Tensor`. May have a different type than
the current `values`.
Returns:
A `SparseTensor` with identical indices and shape but updated values.
Example usage:
>>> st = tf.sparse.from_dense([[1, 0, 2, 0], [3, 0, 0, 4]])
>>> tf.sparse.to_dense(st.with_values([10, 20, 30, 40])) # 4 nonzero values
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[10, 0, 20, 0],
[30, 0, 0, 40]], dtype=int32)>
"""
return SparseTensor(self._indices, new_values, self._dense_shape)
@property
def op(self):
"""The `Operation` that produces `values` as an output."""
return self._values.op
@property
def dtype(self):
"""The `DType` of elements in this tensor."""
return self._values.dtype
@property
def dense_shape(self):
"""A 1-D Tensor of int64 representing the shape of the dense tensor."""
return self._dense_shape
@property
def shape(self):
"""Get the `TensorShape` representing the shape of the dense tensor.
Returns:
A `TensorShape` object.
"""
return self._dense_shape_default
@property
def graph(self):
"""The `Graph` that contains the index, value, and dense_shape tensors."""
return self._indices.graph
def __str__(self):
return "SparseTensor(indices=%s, values=%s, dense_shape=%s)" % (
self._indices, self._values, self._dense_shape)
[文档] def eval(self, feed_dict=None, session=None):
"""Evaluates this sparse tensor in a `Session`.
Calling this method will execute all preceding operations that
produce the inputs needed for the operation that produces this
tensor.
*N.B.* Before invoking `SparseTensor.eval()`, its graph must have been
launched in a session, and either a default session must be
available, or `session` must be specified explicitly.
Args:
feed_dict: A dictionary that maps `Tensor` objects to feed values. See
`tf.Session.run` for a description of the valid feed values.
session: (Optional.) The `Session` to be used to evaluate this sparse
tensor. If none, the default session will be used.
Returns:
A `SparseTensorValue` object.
"""
indices, values, dense_shape = _eval_using_default_session(
[self.indices, self.values, self.dense_shape], feed_dict, self.graph,
session)
return SparseTensorValue(indices, values, dense_shape)
@staticmethod
def _override_operator(operator, func):
_override_helper(SparseTensor, operator, func)
@property
def _type_spec(self):
return SparseTensorSpec(self.shape, self.dtype)
def _shape_invariant_to_type_spec(self, shape):
# From the tf.while_loop docs: "If a loop variable is a SparseTensor, the
# shape invariant must be TensorShape([r]) where r is the rank of the dense
# tensor represented by the sparse tensor. It means the shapes of the three
# tensors of the SparseTensor are ([None], [None, r], [r]). NOTE: The shape
# invariant here is the shape of the SparseTensor.dense_shape property. It
# must be the shape of a vector.
if shape.ndims is not None and shape.ndims != 1:
raise ValueError(f"Expected a shape with 1 dimension. Obtained: {shape} "
f"which has {shape.ndims} dimensions.")
rank = tensor_shape.dimension_value(shape[0])
return SparseTensorSpec(tensor_shape.unknown_shape(rank), self.dtype)
[文档] def consumers(self):
return self._consumers()
SparseTensorValue = collections.namedtuple("SparseTensorValue",
["indices", "values", "dense_shape"])
tf_export(v1=["SparseTensorValue"])(SparseTensorValue)
_pywrap_utils.RegisterType("SparseTensorValue", SparseTensorValue)
[文档]@tf_export("SparseTensorSpec")
@type_spec.register("tf.SparseTensorSpec")
class SparseTensorSpec(type_spec.BatchableTypeSpec):
"""Type specification for a `tf.sparse.SparseTensor`."""
__slots__ = ["_shape", "_dtype"]
value_type = property(lambda self: SparseTensor)
def __init__(self, shape=None, dtype=dtypes.float32):
"""Constructs a type specification for a `tf.sparse.SparseTensor`.
Args:
shape: The dense shape of the `SparseTensor`, or `None` to allow any dense
shape.
dtype: `tf.DType` of values in the `SparseTensor`.
"""
self._shape = tensor_shape.as_shape(shape)
self._dtype = dtypes.as_dtype(dtype)
def _serialize(self):
return (self._shape, self._dtype)
@property
def dtype(self):
"""The `tf.dtypes.DType` specified by this type for the SparseTensor."""
return self._dtype
@property
def shape(self):
"""The `tf.TensorShape` specified by this type for the SparseTensor."""
return self._shape
@property
def _component_specs(self):
rank = self._shape.ndims
num_values = None
return [
tensor_spec.TensorSpec([num_values, rank], dtypes.int64),
tensor_spec.TensorSpec([num_values], self._dtype),
tensor_spec.TensorSpec([rank], dtypes.int64)]
def _to_components(self, value):
if isinstance(value, SparseTensorValue):
value = SparseTensor.from_value(value)
return [value.indices, value.values, value.dense_shape]
def _from_components(self, tensor_list):
if (all(isinstance(t, np.ndarray) for t in tensor_list) and
not tf2.enabled()):
return SparseTensorValue(*tensor_list)
else:
return SparseTensor(*tensor_list)
# The SparseTensorSpec tensor_list encoding uses (de)serialize_sparse ops
# to (un)box the component tensors in a way that allows for batching &
# unbatching.
@property
def _flat_tensor_specs(self):
# NOTE(mrry): The default flat shape of a boxed `SparseTensor` is `(3,)`,
# but a `SparseTensorSpec` can also represent a batch of boxed
# `SparseTensor` objects with shape `(..., 3)` (and batches of batches,
# etc.), so the flat shape must be unknown.
return [tensor_spec.TensorSpec(None, dtypes.variant)]
def _to_tensor_list(self, value):
value = SparseTensor.from_value(value)
return [gen_sparse_ops.serialize_sparse(
value.indices, value.values, value.dense_shape,
out_type=dtypes.variant)]
def _to_batched_tensor_list(self, value):
dense_shape = tensor_util.constant_value_as_shape(value.dense_shape)
if self._shape.merge_with(dense_shape).ndims == 0:
raise ValueError(
"Unbatching a sparse tensor is only supported for rank >= 1. "
f"Obtained input: {value}.")
return [gen_sparse_ops.serialize_many_sparse(
value.indices, value.values, value.dense_shape,
out_type=dtypes.variant)]
def _from_compatible_tensor_list(self, tensor_list):
tensor_list = gen_sparse_ops.deserialize_sparse(tensor_list[0], self._dtype)
indices, values, dense_shape = tensor_list
rank = self._shape.ndims
indices.set_shape([None, rank])
# We restore the dense_shape from the SparseTypeSpec. This is necessary
# for shape inference when using placeholder SparseTensors in function
# tracing.
if self._shape.is_fully_defined():
dense_shape = ops.convert_to_tensor(
self._shape, dtype=dtypes.int64, name="shape")
elif (self._shape.rank is not None and
any(dim.value is not None for dim in self._shape.dims)):
# array_ops imports sparse_tensor.py. Local import to avoid import cycle.
from tensorflow.python.ops import array_ops # pylint: disable=g-import-not-at-top
pieces = array_ops.unstack(dense_shape, num=self._shape.rank)
for i, dim in enumerate(self._shape.dims):
if dim.value is not None:
pieces[i] = constant_op.constant(dim.value, dense_shape.dtype)
dense_shape = array_ops.stack(pieces)
else:
dense_shape.set_shape([rank])
return SparseTensor(indices, values, dense_shape)
def _batch(self, batch_size):
return SparseTensorSpec(
tensor_shape.TensorShape([batch_size]).concatenate(self._shape),
self._dtype)
def _unbatch(self):
if self._shape.ndims == 0:
raise ValueError("Unbatching a tensor is only supported for rank >= 1")
return SparseTensorSpec(self._shape[1:], self._dtype)
def _to_legacy_output_types(self):
return self._dtype
def _to_legacy_output_shapes(self):
return self._shape
def _to_legacy_output_classes(self):
return SparseTensor
[文档] @classmethod
def from_value(cls, value):
if isinstance(value, SparseTensor):
return cls(value.shape, value.dtype)
if isinstance(value, SparseTensorValue):
if isinstance(value.values, np.ndarray):
return cls(value.dense_shape, value.values.dtype)
else:
return cls.from_value(SparseTensor.from_value(value))
else:
raise TypeError("Expected SparseTensor or SparseTensorValue. Received: "
f"{value} of type {type(value).__name__}.")
# TODO(b/133606651) Delete the SparseTensor registration when CompositeTensor
# is updated to define a _type_spec field (since registration will be
# automatic). Do *not* delete the SparseTensorValue registration.
type_spec.register_type_spec_from_value_converter(
SparseTensor, SparseTensorSpec.from_value)
type_spec.register_type_spec_from_value_converter(
SparseTensorValue, SparseTensorSpec.from_value)
@tf_export(v1=["convert_to_tensor_or_sparse_tensor"])
def convert_to_tensor_or_sparse_tensor(value, dtype=None, name=None):
"""Converts value to a `SparseTensor` or `Tensor`.
Args:
value: A `SparseTensor`, `SparseTensorValue`, or an object whose type has a
registered `Tensor` conversion function.
dtype: Optional element type for the returned tensor. If missing, the type
is inferred from the type of `value`.
name: Optional name to use if a new `Tensor` is created.
Returns:
A `SparseTensor` or `Tensor` based on `value`.
Raises:
RuntimeError: If result type is incompatible with `dtype`.
"""
if dtype is not None:
dtype = dtypes.as_dtype(dtype)
if isinstance(value, SparseTensorValue):
value = SparseTensor.from_value(value)
if isinstance(value, SparseTensor):
if dtype and not dtype.is_compatible_with(value.dtype):
raise RuntimeError(f"Sparse dtype mismatch. Requested: {dtype.name}, "
f" Actual: {value.dtype.name}")
return value
return ops.convert_to_tensor(value, dtype=dtype, name=name)
def is_sparse(x):
"""Check whether `x` is sparse.
Check whether an object is a `tf.sparse.SparseTensor` or
`tf.compat.v1.SparseTensorValue`.
Args:
x: A python object to check.
Returns:
`True` iff `x` is a `tf.sparse.SparseTensor` or
`tf.compat.v1.SparseTensorValue`.
"""
return isinstance(x, (SparseTensor, SparseTensorValue))