Intelegentny_Pszczelarz/.venv/Lib/site-packages/tensorflow/python/framework/composite_tensor.py

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# Copyright 2019 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.
# ==============================================================================
"""Tensor-like objects that are composed from tf.Tensors."""
import abc
from tensorflow.python import pywrap_tensorflow # pylint: disable=unused-import
from tensorflow.python.util import _pywrap_utils
from tensorflow.python.util import nest
from tensorflow.python.util.tf_export import tf_export
@tf_export("__internal__.CompositeTensor", v1=[])
class CompositeTensor(metaclass=abc.ABCMeta):
"""Abstract base class for Tensor-like objects that are composed from Tensors.
Each `CompositeTensor` can be decomposed into a structured collection of
component `tf.Tensor`s, and reconstructed from those components.
The `tensorflow.python.util.nest` module has support for treating composite
tensors as structure, which makes it easy to flatten and reconstruct
composite tensors (or larger structures that contain composite tensors).
E.g.:
```python
ct = ... # Create a composite tensor.
flat_list_of_tensors = nest.flatten(ct, expand_composites=True)
transformed_list_of_tensors = ... # do something with the flat tensors.
result = nest.pack_sequence_as(ct, transformed_list_of_tensors,
expand_composites=True)
```
"""
@abc.abstractproperty
def _type_spec(self):
"""A `TypeSpec` describing the type of this value."""
raise NotImplementedError(f"{type(self).__name__}._type_spec()")
def _shape_invariant_to_type_spec(self, shape):
"""Returns a TypeSpec given a shape invariant (used by `tf.while_loop`).
Args:
shape: A `tf.TensorShape` object. The shape invariant for this
`CompositeTensor`, or `None` if a default shape invariant should be used
(based on the value of this `CompositeTensor`).
Returns:
A nested structure whose values are `tf.TensorShape` objects, specifying
the shape invariants for the tensors that comprise this `CompositeTensor`.
"""
# New TypeSpec subclasses generally do not need to implement this --
# this method is used for backwards compatibility. Users of tf.while_loop
# can specify a type by passing in TypeSpec instead.
raise NotImplementedError(
f"{type(self).__name__}._shape_invariant_to_type_spec")
def _consumers(self):
"""Returns a list of `Operation`s that consume this `CompositeTensor`.
Returns:
A list of `Operation`s.
Raises:
RuntimeError: If this method is called while executing eagerly.
"""
consumers = nest.flatten([
component.consumers()
for component in nest.flatten(self, expand_composites=True)
if getattr(component, "graph", None) is not None
])
return list(set(consumers))
def __tf_tracing_type__(self, context):
return self._type_spec.__tf_tracing_type__(context)
def _convert_variables_to_tensors(self):
"""Converts ResourceVariable components to Tensors.
Override this method to explicitly convert ResourceVariables embedded in the
CompositeTensor to Tensors. By default, it returns the CompositeTensor
unchanged.
Returns:
A CompositeTensor with all its ResourceVariable components converted to
Tensors.
"""
return self
_pywrap_utils.RegisterType("CompositeTensor", CompositeTensor)
def replace_composites_with_components(structure):
"""Recursively replaces CompositeTensors with their components.
Args:
structure: A `nest`-compatible structure, possibly containing composite
tensors.
Returns:
A copy of `structure`, where each composite tensor has been replaced by
its components. The result will contain no composite tensors.
Note that `nest.flatten(replace_composites_with_components(structure))`
returns the same value as `nest.flatten(structure)`.
"""
if isinstance(structure, CompositeTensor):
return replace_composites_with_components(
structure._type_spec._to_components(structure)) # pylint: disable=protected-access
elif not nest.is_nested(structure):
return structure
else:
return nest.map_structure(
replace_composites_with_components, structure, expand_composites=False)
def convert_variables_to_tensors(composite_tensor):
return composite_tensor._convert_variables_to_tensors() # pylint: disable=protected-access
# @TODO(edloper): Can we replace convert_to_tensor_or_xyz with just
# convert_to_tensor_or_composite? Alternatively, should composite tensors
# register a dispatch override for tf.convert_to_tensor?
# Note about the internal encoding of composite tensors when they are "lowered"
# from Python objects to tensors. The usual encoding is "component encoding"
# which uses the dense tensors that represent a composite tensor.
# A second encoding, "batchable tensor list encoding", is used by datasets
# and map_fn which in addition to supporting batching also can use ops
# for encoding and decoding, e.g. for encoding/decoding to/from a
# single variant that represents a composite tensor. Some internal properties
# for type specs for composite tensors use `flat` as a nickname for
# "batchable tensor list encoding". (e.g. `flat_tensor_specs`).