1740 lines
65 KiB
Python
1740 lines
65 KiB
Python
# Copyright 2021 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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"""Functions that work with structures.
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A structure is either:
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* one of the recognized Python collections, holding _nested structures_;
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* a value of any other type, typically a TensorFlow data type like Tensor,
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Variable, or of compatible types such as int, float, ndarray, etc. these are
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commonly referred to as _atoms_ of the structure.
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A structure of type `T` is a structure whose atomic items are of type `T`.
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For example, a structure of `tf.Tensor` only contains `tf.Tensor` as its atoms.
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Historically a _nested structure_ was called a _nested sequence_ in TensorFlow.
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A nested structure is sometimes called a _nest_ or a _tree_, but the formal
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name _nested structure_ is preferred.
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Refer to [Nesting Data Structures]
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(https://en.wikipedia.org/wiki/Nesting_(computing)#Data_structures).
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The following collection types are recognized by `tf.nest` as nested
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structures:
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* `collections.abc.Sequence` (except `string` and `bytes`).
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This includes `list`, `tuple`, and `namedtuple`.
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* `collections.abc.Mapping` (with sortable keys).
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This includes `dict` and `collections.OrderedDict`.
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* `collections.abc.MappingView` (with sortable keys).
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* [`attr.s` classes](https://www.attrs.org/).
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Any other values are considered **atoms**. Not all collection types are
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considered nested structures. For example, the following types are
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considered atoms:
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* `set`; `{"a", "b"}` is an atom, while `["a", "b"]` is a nested structure.
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* [`dataclass` classes](https://docs.python.org/library/dataclasses.html)
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* `tf.Tensor`
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* `numpy.array`
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`tf.nest.is_nested` checks whether an object is a nested structure or an atom.
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For example:
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>>> tf.nest.is_nested("1234")
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False
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>>> tf.nest.is_nested([1, 3, [4, 5]])
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True
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>>> tf.nest.is_nested(((7, 8), (5, 6)))
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True
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>>> tf.nest.is_nested([])
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True
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>>> tf.nest.is_nested({"a": 1, "b": 2})
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True
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>>> tf.nest.is_nested({"a": 1, "b": 2}.keys())
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True
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>>> tf.nest.is_nested({"a": 1, "b": 2}.values())
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True
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>>> tf.nest.is_nested({"a": 1, "b": 2}.items())
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True
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>>> tf.nest.is_nested(set([1, 2]))
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False
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>>> ones = tf.ones([2, 3])
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>>> tf.nest.is_nested(ones)
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False
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Note: A proper structure shall form a tree. The user shall ensure there is no
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cyclic references within the items in the structure,
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i.e., no references in the structure of the input of these functions
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should be recursive. The behavior is undefined if there is a cycle.
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"""
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import collections as _collections
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import six as _six
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import wrapt as _wrapt
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from tensorflow.python.platform import tf_logging
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from tensorflow.python.util import _pywrap_nest
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from tensorflow.python.util import _pywrap_utils
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from tensorflow.python.util.compat import collections_abc as _collections_abc
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from tensorflow.python.util.tf_export import tf_export
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_SHALLOW_TREE_HAS_INVALID_KEYS = (
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"The shallow_tree's keys are not a subset of the input_tree's keys. The "
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"shallow_tree has the following keys that are not in the input_tree: {}.")
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_STRUCTURES_HAVE_MISMATCHING_TYPES = (
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"The two structures don't have the same sequence type. Input structure has "
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"type {input_type}, while shallow structure has type {shallow_type}.")
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_STRUCTURES_HAVE_MISMATCHING_LENGTHS = (
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"The two structures don't have the same sequence length. Input "
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"structure has length {input_length}, while shallow structure has length "
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"{shallow_length}."
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)
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_INPUT_TREE_SMALLER_THAN_SHALLOW_TREE = (
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"The input_tree has fewer items than the shallow_tree. Input structure "
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"has length {input_size}, while shallow structure has length "
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"{shallow_size}.")
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_IF_SHALLOW_IS_SEQ_INPUT_MUST_BE_SEQ = (
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"If shallow structure is a sequence, input must also be a sequence. "
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"Input has type: {}.")
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def _get_attrs_items(obj):
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"""Returns a list of (name, value) pairs from an attrs instance.
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The list will be sorted by name.
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Args:
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obj: an object.
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Returns:
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A list of (attr_name, attr_value) pairs, sorted by attr_name.
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"""
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attrs = getattr(obj.__class__, "__attrs_attrs__")
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attr_names = (a.name for a in attrs)
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return [(attr_name, getattr(obj, attr_name)) for attr_name in attr_names]
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def _sorted(dict_):
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"""Returns a sorted list of the dict keys, with error if keys not sortable."""
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try:
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return sorted(dict_.keys())
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except TypeError:
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raise TypeError("nest only supports dicts with sortable keys.")
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# TODO(b/225045380): Move to a "leaf" library to use in trace_type.
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def is_namedtuple(instance, strict=False):
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"""Returns True iff `instance` is a `namedtuple`.
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Args:
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instance: An instance of a Python object.
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strict: If True, `instance` is considered to be a `namedtuple` only if
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it is a "plain" namedtuple. For instance, a class inheriting
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from a `namedtuple` will be considered to be a `namedtuple`
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iff `strict=False`.
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Returns:
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True if `instance` is a `namedtuple`.
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"""
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return _pywrap_utils.IsNamedtuple(instance, strict)
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_is_namedtuple = is_namedtuple # This function was private up to TF2.5.
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_is_mapping_view = _pywrap_utils.IsMappingView
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_is_attrs = _pywrap_utils.IsAttrs
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_is_composite_tensor = _pywrap_utils.IsCompositeTensor
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_is_type_spec = _pywrap_utils.IsTypeSpec
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_is_mutable_mapping = _pywrap_utils.IsMutableMapping
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_is_mapping = _pywrap_utils.IsMapping
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# TODO(b/225045380): Move to a "leaf" library to use in trace_type.
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@tf_export("__internal__.nest.is_attrs", v1=[])
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def is_attrs(obj):
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"""Returns a true if its input is an instance of an attr.s decorated class."""
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return _is_attrs(obj)
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@tf_export("__internal__.nest.is_mapping", v1=[])
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def is_mapping(obj):
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"""Returns a true if its input is a collections.Mapping."""
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return _is_mapping(obj)
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@tf_export("__internal__.nest.sequence_like", v1=[])
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def _sequence_like(instance, args):
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"""Converts the sequence `args` to the same type as `instance`.
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Args:
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instance: an instance of `tuple`, `list`, `namedtuple`, `dict`,
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`collections.OrderedDict`, or `composite_tensor.Composite_Tensor`
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or `type_spec.TypeSpec`.
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args: items to be converted to the `instance` type.
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Returns:
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`args` with the type of `instance`.
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"""
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if _is_mutable_mapping(instance):
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# Pack dictionaries in a deterministic order by sorting the keys.
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# Notice this means that we ignore the original order of `OrderedDict`
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# instances. This is intentional, to avoid potential bugs caused by mixing
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# ordered and plain dicts (e.g., flattening a dict but using a
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# corresponding `OrderedDict` to pack it back).
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result = dict(zip(_sorted(instance), args))
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instance_type = type(instance)
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if instance_type == _collections.defaultdict:
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d = _collections.defaultdict(instance.default_factory)
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else:
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d = instance_type()
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for key in instance:
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d[key] = result[key]
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return d
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elif _is_mapping(instance):
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result = dict(zip(_sorted(instance), args))
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instance_type = type(instance)
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if not getattr(instance_type, "__supported_by_tf_nest__", False):
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tf_logging.log_first_n(
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tf_logging.WARN, "Mapping types may not work well with tf.nest. "
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"Prefer using MutableMapping for {}".format(instance_type), 1)
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try:
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return instance_type((key, result[key]) for key in instance)
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except TypeError as err:
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raise TypeError("Error creating an object of type {} like {}. Note that "
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"it must accept a single positional argument "
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"representing an iterable of key-value pairs, in "
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"addition to self. Cause: {}".format(
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type(instance), instance, err))
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elif _is_mapping_view(instance):
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# We can't directly construct mapping views, so we create a list instead
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return list(args)
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elif is_namedtuple(instance) or _is_attrs(instance):
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if isinstance(instance, _wrapt.ObjectProxy):
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instance_type = type(instance.__wrapped__)
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else:
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instance_type = type(instance)
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return instance_type(*args)
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elif _is_composite_tensor(instance):
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assert len(args) == 1
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spec = instance._type_spec # pylint: disable=protected-access
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return spec._from_components(args[0]) # pylint: disable=protected-access
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elif _is_type_spec(instance):
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# Pack a CompositeTensor's components according to a TypeSpec.
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assert len(args) == 1
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return instance._from_components(args[0]) # pylint: disable=protected-access
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elif isinstance(instance, _six.moves.range):
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return _sequence_like(list(instance), args)
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elif isinstance(instance, _wrapt.ObjectProxy):
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# For object proxies, first create the underlying type and then re-wrap it
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# in the proxy type.
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return type(instance)(_sequence_like(instance.__wrapped__, args))
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else:
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# Not a namedtuple
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return type(instance)(args)
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def _yield_value(iterable):
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for _, v in _yield_sorted_items(iterable):
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yield v
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def _yield_sorted_items(iterable):
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"""Yield (key, value) pairs for `iterable` in a deterministic order.
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For Sequences, the key will be an int, the array index of a value.
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For Mappings, the key will be the dictionary key.
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For objects (e.g. namedtuples), the key will be the attribute name.
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In all cases, the keys will be iterated in sorted order.
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Args:
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iterable: an iterable.
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Yields:
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The iterable's (key, value) pairs, in order of sorted keys.
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"""
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# Ordered to check common structure types (list, tuple, dict) first.
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if isinstance(iterable, list):
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for item in enumerate(iterable):
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yield item
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# namedtuples handled separately to avoid expensive namedtuple check.
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elif type(iterable) == tuple: # pylint: disable=unidiomatic-typecheck
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for item in enumerate(iterable):
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yield item
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elif isinstance(iterable, (dict, _collections_abc.Mapping)):
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# Iterate through dictionaries in a deterministic order by sorting the
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# keys. Notice this means that we ignore the original order of `OrderedDict`
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# instances. This is intentional, to avoid potential bugs caused by mixing
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# ordered and plain dicts (e.g., flattening a dict but using a
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# corresponding `OrderedDict` to pack it back).
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for key in _sorted(iterable):
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yield key, iterable[key]
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elif _is_attrs(iterable):
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for item in _get_attrs_items(iterable):
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yield item
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elif is_namedtuple(iterable):
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for field in iterable._fields:
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yield field, getattr(iterable, field)
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elif _is_composite_tensor(iterable):
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type_spec = iterable._type_spec # pylint: disable=protected-access
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yield type_spec.value_type.__name__, type_spec._to_components(iterable) # pylint: disable=protected-access
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elif _is_type_spec(iterable):
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# Note: to allow CompositeTensors and their TypeSpecs to have matching
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# structures, we need to use the same key string here.
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yield iterable.value_type.__name__, iterable._component_specs # pylint: disable=protected-access
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else:
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for item in enumerate(iterable):
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yield item
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_is_nested = _pywrap_utils.IsNested
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_is_nested_or_composite = _pywrap_utils.IsNestedOrComposite
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@tf_export("nest.is_nested")
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def is_nested(seq):
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"""Returns true if its input is a nested structure.
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Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
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for the definition of a nested structure.
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Args:
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seq: the value to test.
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Returns:
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True if the input is a nested structure.
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"""
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return _is_nested(seq)
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def is_nested_or_composite(seq):
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"""Returns true if its input is a nested structure or a composite.
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Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
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for the definition of a nested structure.
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Args:
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seq: the value to test.
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Returns:
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True if the input is a nested structure or a composite.
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"""
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return _is_nested_or_composite(seq)
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# FIXME(feyu): Remove the back-compat names before closing b/201685523, after
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# all users of is_sequence are moved to the new names. (cl/405503918)
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def is_sequence(seq):
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return _is_nested(seq)
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def is_sequence_or_composite(seq):
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return _is_nested_or_composite(seq)
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@tf_export("nest.flatten")
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def flatten(structure, expand_composites=False):
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"""Returns a flat list from a given structure.
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Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
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for the definition of a structure.
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If the structure is an atom, then returns a single-item list: [structure].
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This is the inverse of the `nest.pack_sequence_as` method that takes in a
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flattened list and re-packs it into the nested structure.
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In the case of dict instances, the sequence consists of the values, sorted by
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key to ensure deterministic behavior. This is true also for OrderedDict
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instances: their sequence order is ignored, the sorting order of keys is used
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instead. The same convention is followed in `nest.pack_sequence_as`. This
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correctly repacks dicts and OrderedDicts after they have been flattened, and
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also allows flattening an OrderedDict and then repacking it back using a
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corresponding plain dict, or vice-versa. Dictionaries with non-sortable keys
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cannot be flattened.
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Users must not modify any collections used in nest while this function is
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running.
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Examples:
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1. Python dict (ordered by key):
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>>> dict = { "key3": "value3", "key1": "value1", "key2": "value2" }
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>>> tf.nest.flatten(dict)
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['value1', 'value2', 'value3']
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2. For a nested python tuple:
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>>> tuple = ((1.0, 2.0), (3.0, 4.0, 5.0), 6.0)
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>>> tf.nest.flatten(tuple)
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[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
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3. For a nested dictionary of dictionaries:
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>>> dict = { "key3": {"c": (1.0, 2.0), "a": (3.0)},
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... "key1": {"m": "val1", "g": "val2"} }
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>>> tf.nest.flatten(dict)
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['val2', 'val1', 3.0, 1.0, 2.0]
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4. Numpy array (will not flatten):
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>>> array = np.array([[1, 2], [3, 4]])
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>>> tf.nest.flatten(array)
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[array([[1, 2],
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[3, 4]])]
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5. `tf.Tensor` (will not flatten):
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>>> tensor = tf.constant([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]])
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>>> tf.nest.flatten(tensor)
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[<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
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array([[1., 2., 3.],
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[4., 5., 6.],
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[7., 8., 9.]], dtype=float32)>]
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6. `tf.RaggedTensor`: This is a composite tensor thats representation consists
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of a flattened list of 'values' and a list of 'row_splits' which indicate how
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to chop up the flattened list into different rows. For more details on
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`tf.RaggedTensor`, please visit
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https://www.tensorflow.org/api_docs/python/tf/RaggedTensor.
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with `expand_composites=False`, we just return the RaggedTensor as is.
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>>> tensor = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2]])
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>>> tf.nest.flatten(tensor, expand_composites=False)
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[<tf.RaggedTensor [[3, 1, 4, 1], [], [5, 9, 2]]>]
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with `expand_composites=True`, we return the component Tensors that make up
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the RaggedTensor representation (the values and row_splits tensors)
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>>> tensor = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2]])
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>>> tf.nest.flatten(tensor, expand_composites=True)
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[<tf.Tensor: shape=(7,), dtype=int32, numpy=array([3, 1, 4, 1, 5, 9, 2],
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dtype=int32)>,
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<tf.Tensor: shape=(4,), dtype=int64, numpy=array([0, 4, 4, 7])>]
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Args:
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structure: an atom or a nested structure. Note, numpy arrays are considered
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atoms and are not flattened.
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expand_composites: If true, then composite tensors such as
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`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
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component tensors.
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Returns:
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A Python list, the flattened version of the input.
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Raises:
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TypeError: The nest is or contains a dict with non-sortable keys.
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"""
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if structure is None:
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return [None]
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expand_composites = bool(expand_composites)
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return _pywrap_utils.Flatten(structure, expand_composites)
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# See the swig file (util.i) for documentation.
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same_namedtuples = _pywrap_utils.SameNamedtuples
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_same_namedtuples = same_namedtuples # This function was private up to TF2.5.
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class _DotString(object):
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__slots__ = []
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def __str__(self):
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return "."
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def __repr__(self):
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return "."
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_DOT = _DotString()
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@tf_export("nest.assert_same_structure")
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def assert_same_structure(nest1, nest2, check_types=True,
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expand_composites=False):
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"""Asserts that two structures are nested in the same way.
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|
|
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
|
|
for the definition of a structure.
|
|
|
|
Note the method does not check the types of atoms inside the structures.
|
|
|
|
Examples:
|
|
|
|
* These atom vs. atom comparisons will pass:
|
|
|
|
>>> tf.nest.assert_same_structure(1.5, tf.Variable(1, tf.uint32))
|
|
>>> tf.nest.assert_same_structure("abc", np.array([1, 2]))
|
|
|
|
* These nested structure vs. nested structure comparisons will pass:
|
|
|
|
>>> structure1 = (((1, 2), 3), 4, (5, 6))
|
|
>>> structure2 = ((("foo1", "foo2"), "foo3"), "foo4", ("foo5", "foo6"))
|
|
>>> structure3 = [(("a", "b"), "c"), "d", ["e", "f"]]
|
|
>>> tf.nest.assert_same_structure(structure1, structure2)
|
|
>>> tf.nest.assert_same_structure(structure1, structure3, check_types=False)
|
|
|
|
>>> import collections
|
|
>>> tf.nest.assert_same_structure(
|
|
... collections.namedtuple("bar", "a b")(1, 2),
|
|
... collections.namedtuple("foo", "a b")(2, 3),
|
|
... check_types=False)
|
|
|
|
>>> tf.nest.assert_same_structure(
|
|
... collections.namedtuple("bar", "a b")(1, 2),
|
|
... { "a": 1, "b": 2 },
|
|
... check_types=False)
|
|
|
|
>>> tf.nest.assert_same_structure(
|
|
... { "a": 1, "b": 2, "c": 3 },
|
|
... { "c": 6, "b": 5, "a": 4 })
|
|
|
|
>>> ragged_tensor1 = tf.RaggedTensor.from_row_splits(
|
|
... values=[3, 1, 4, 1, 5, 9, 2, 6],
|
|
... row_splits=[0, 4, 4, 7, 8, 8])
|
|
>>> ragged_tensor2 = tf.RaggedTensor.from_row_splits(
|
|
... values=[3, 1, 4],
|
|
... row_splits=[0, 3])
|
|
>>> tf.nest.assert_same_structure(
|
|
... ragged_tensor1,
|
|
... ragged_tensor2,
|
|
... expand_composites=True)
|
|
|
|
* These examples will raise exceptions:
|
|
|
|
>>> tf.nest.assert_same_structure([0, 1], np.array([0, 1]))
|
|
Traceback (most recent call last):
|
|
...
|
|
ValueError: The two structures don't have the same nested structure
|
|
|
|
>>> tf.nest.assert_same_structure(
|
|
... collections.namedtuple('bar', 'a b')(1, 2),
|
|
... collections.namedtuple('foo', 'a b')(2, 3))
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: The two structures don't have the same nested structure
|
|
|
|
Args:
|
|
nest1: an atom or a nested structure.
|
|
nest2: an atom or a nested structure.
|
|
check_types: if `True` (default) types of structures are checked as well,
|
|
including the keys of dictionaries. If set to `False`, for example a list
|
|
and a tuple of objects will look the same if they have the same size. Note
|
|
that namedtuples with identical name and fields are always considered to
|
|
have the same shallow structure. Two types will also be considered the
|
|
same if they are both list subtypes (which allows "list" and
|
|
"_ListWrapper" from trackable dependency tracking to compare equal).
|
|
`check_types=True` only checks type of sub-structures. The types of atoms
|
|
are not checked.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Raises:
|
|
ValueError: If the two structures do not have the same number of atoms or
|
|
if the two structures are not nested in the same way.
|
|
TypeError: If the two structures differ in the type of sequence in any of
|
|
their substructures. Only possible if `check_types` is `True`.
|
|
"""
|
|
# Convert to bool explicitly as otherwise pybind will not be able# to handle
|
|
# type mismatch message correctly. See GitHub issue 42329 for details.
|
|
check_types = bool(check_types)
|
|
expand_composites = bool(expand_composites)
|
|
try:
|
|
_pywrap_utils.AssertSameStructure(nest1, nest2, check_types,
|
|
expand_composites)
|
|
except (ValueError, TypeError) as e:
|
|
str1 = str(map_structure(lambda _: _DOT, nest1))
|
|
str2 = str(map_structure(lambda _: _DOT, nest2))
|
|
raise type(e)("%s\n"
|
|
"Entire first structure:\n%s\n"
|
|
"Entire second structure:\n%s"
|
|
% (str(e), str1, str2))
|
|
|
|
|
|
def flatten_dict_items(dictionary):
|
|
"""Returns a dictionary with flattened keys and values.
|
|
|
|
This function flattens the keys and values of a dictionary, which can be
|
|
arbitrarily nested structures, and returns the flattened version of such
|
|
structures:
|
|
|
|
```python
|
|
example_dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", "d"))}
|
|
result = {4: "a", 5: "b", 6: "c", 8: "d"}
|
|
flatten_dict_items(example_dictionary) == result
|
|
```
|
|
|
|
The input dictionary must satisfy two properties:
|
|
|
|
1. Its keys and values should have the same exact nested structure.
|
|
2. The set of all flattened keys of the dictionary must not contain repeated
|
|
keys.
|
|
|
|
Args:
|
|
dictionary: the dictionary to zip
|
|
|
|
Returns:
|
|
The zipped dictionary.
|
|
|
|
Raises:
|
|
TypeError: If the input is not a dictionary.
|
|
ValueError: If any key and value do not have the same structure layout, or
|
|
if keys are not unique.
|
|
"""
|
|
return _pywrap_nest.FlattenDictItems(dictionary)
|
|
|
|
|
|
def _packed_nest_with_indices(structure,
|
|
flat,
|
|
index,
|
|
is_nested_fn,
|
|
sequence_fn=None):
|
|
"""Helper function for pack_sequence_as.
|
|
|
|
Args:
|
|
structure: structure to mimic.
|
|
flat: Flattened values to output substructure for.
|
|
index: Index at which to start reading from flat.
|
|
is_nested_fn: Function used to test if a value should be treated as a
|
|
nested structure.
|
|
sequence_fn: Function used to generate a new strcuture instance.
|
|
|
|
Returns:
|
|
The tuple (new_index, child), where:
|
|
* new_index - the updated index into `flat` having processed `structure`.
|
|
* packed - the subset of `flat` corresponding to `structure`,
|
|
having started at `index`, and packed into the same nested
|
|
format.
|
|
|
|
Raises:
|
|
ValueError: if `structure` contains more atoms than `flat`
|
|
(assuming indexing starts from `index`).
|
|
"""
|
|
packed = []
|
|
sequence_fn = sequence_fn or _sequence_like
|
|
for s in _yield_value(structure):
|
|
if is_nested_fn(s):
|
|
new_index, child = _packed_nest_with_indices(s, flat, index, is_nested_fn,
|
|
sequence_fn)
|
|
packed.append(sequence_fn(s, child))
|
|
index = new_index
|
|
else:
|
|
packed.append(flat[index])
|
|
index += 1
|
|
return index, packed
|
|
|
|
|
|
def _pack_sequence_as(structure, flat_sequence, expand_composites,
|
|
sequence_fn=None):
|
|
"""Implements sequence packing, with the option to alter the structure."""
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
sequence_fn = sequence_fn or _sequence_like
|
|
def truncate(value, length):
|
|
value_str = str(value)
|
|
return value_str[:length] + (value_str[length:] and "...")
|
|
|
|
if not is_nested_fn(flat_sequence):
|
|
raise TypeError(
|
|
"Attempted to pack value:\n {}\ninto a structure, but found "
|
|
"incompatible type `{}` instead.".format(
|
|
truncate(flat_sequence, 100), type(flat_sequence)))
|
|
|
|
if not is_nested_fn(structure):
|
|
if len(flat_sequence) != 1:
|
|
raise ValueError(
|
|
"The target structure is of type `{}`\n {}\nHowever the input "
|
|
"is a sequence ({}) of length {}.\n {}\nnest cannot "
|
|
"guarantee that it is safe to map one to the other.".format(
|
|
type(structure), truncate(structure, 100), type(flat_sequence),
|
|
len(flat_sequence), truncate(flat_sequence, 100)))
|
|
return flat_sequence[0]
|
|
|
|
try:
|
|
final_index, packed = _packed_nest_with_indices(structure, flat_sequence, 0,
|
|
is_nested_fn, sequence_fn)
|
|
if final_index < len(flat_sequence):
|
|
raise IndexError
|
|
except IndexError:
|
|
flat_structure = flatten(structure, expand_composites=expand_composites)
|
|
if len(flat_structure) != len(flat_sequence):
|
|
raise ValueError(
|
|
"Could not pack sequence. Structure had %d atoms, but "
|
|
"flat_sequence had %d items. Structure: %s, flat_sequence: %s." %
|
|
(len(flat_structure), len(flat_sequence), structure, flat_sequence))
|
|
return sequence_fn(structure, packed)
|
|
|
|
|
|
@tf_export("nest.pack_sequence_as")
|
|
def pack_sequence_as(structure, flat_sequence, expand_composites=False):
|
|
"""Returns a given flattened sequence packed into a given structure.
|
|
|
|
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
|
|
for the definition of a structure.
|
|
|
|
If `structure` is an atom, `flat_sequence` must be a single-item list;
|
|
in this case the return value is `flat_sequence[0]`.
|
|
|
|
If `structure` is or contains a dict instance, the keys will be sorted to
|
|
pack the flat sequence in deterministic order. This is true also for
|
|
`OrderedDict` instances: their sequence order is ignored, the sorting order of
|
|
keys is used instead. The same convention is followed in `flatten`.
|
|
This correctly repacks dicts and `OrderedDict`s after they have been
|
|
flattened, and also allows flattening an `OrderedDict` and then repacking it
|
|
back using a corresponding plain dict, or vice-versa.
|
|
Dictionaries with non-sortable keys cannot be flattened.
|
|
|
|
Examples:
|
|
|
|
1. Python dict:
|
|
|
|
>>> structure = { "key3": "", "key1": "", "key2": "" }
|
|
>>> flat_sequence = ["value1", "value2", "value3"]
|
|
>>> tf.nest.pack_sequence_as(structure, flat_sequence)
|
|
{'key3': 'value3', 'key1': 'value1', 'key2': 'value2'}
|
|
|
|
2. For a nested python tuple:
|
|
|
|
>>> structure = (('a','b'), ('c','d','e'), 'f')
|
|
>>> flat_sequence = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
|
|
>>> tf.nest.pack_sequence_as(structure, flat_sequence)
|
|
((1.0, 2.0), (3.0, 4.0, 5.0), 6.0)
|
|
|
|
3. For a nested dictionary of dictionaries:
|
|
|
|
>>> structure = { "key3": {"c": ('alpha', 'beta'), "a": ('gamma')},
|
|
... "key1": {"e": "val1", "d": "val2"} }
|
|
>>> flat_sequence = ['val2', 'val1', 3.0, 1.0, 2.0]
|
|
>>> tf.nest.pack_sequence_as(structure, flat_sequence)
|
|
{'key3': {'c': (1.0, 2.0), 'a': 3.0}, 'key1': {'e': 'val1', 'd': 'val2'}}
|
|
|
|
4. Numpy array (considered a scalar):
|
|
|
|
>>> structure = ['a']
|
|
>>> flat_sequence = [np.array([[1, 2], [3, 4]])]
|
|
>>> tf.nest.pack_sequence_as(structure, flat_sequence)
|
|
[array([[1, 2],
|
|
[3, 4]])]
|
|
|
|
5. tf.Tensor (considered a scalar):
|
|
|
|
>>> structure = ['a']
|
|
>>> flat_sequence = [tf.constant([[1., 2., 3.], [4., 5., 6.]])]
|
|
>>> tf.nest.pack_sequence_as(structure, flat_sequence)
|
|
[<tf.Tensor: shape=(2, 3), dtype=float32,
|
|
numpy= array([[1., 2., 3.], [4., 5., 6.]], dtype=float32)>]
|
|
|
|
6. `tf.RaggedTensor`: This is a composite tensor thats representation consists
|
|
of a flattened list of 'values' and a list of 'row_splits' which indicate how
|
|
to chop up the flattened list into different rows. For more details on
|
|
`tf.RaggedTensor`, please visit
|
|
https://www.tensorflow.org/api_docs/python/tf/RaggedTensor.
|
|
|
|
With `expand_composites=False`, we treat RaggedTensor as a scalar.
|
|
|
|
>>> structure = { "foo": tf.ragged.constant([[1, 2], [3]]),
|
|
... "bar": tf.constant([[5]]) }
|
|
>>> flat_sequence = [ "one", "two" ]
|
|
>>> tf.nest.pack_sequence_as(structure, flat_sequence,
|
|
... expand_composites=False)
|
|
{'foo': 'two', 'bar': 'one'}
|
|
|
|
With `expand_composites=True`, we expect that the flattened input contains
|
|
the tensors making up the ragged tensor i.e. the values and row_splits
|
|
tensors.
|
|
|
|
>>> structure = { "foo": tf.ragged.constant([[1., 2.], [3.]]),
|
|
... "bar": tf.constant([[5.]]) }
|
|
>>> tensors = tf.nest.flatten(structure, expand_composites=True)
|
|
>>> print(tensors)
|
|
[<tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[5.]],
|
|
dtype=float32)>,
|
|
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([1., 2., 3.],
|
|
dtype=float32)>,
|
|
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([0, 2, 3])>]
|
|
>>> verified_tensors = [tf.debugging.check_numerics(t, 'invalid tensor: ')
|
|
... if t.dtype==tf.float32 else t
|
|
... for t in tensors]
|
|
>>> tf.nest.pack_sequence_as(structure, verified_tensors,
|
|
... expand_composites=True)
|
|
{'foo': <tf.RaggedTensor [[1.0, 2.0], [3.0]]>,
|
|
'bar': <tf.Tensor: shape=(1, 1), dtype=float32, numpy=array([[5.]],
|
|
dtype=float32)>}
|
|
|
|
Args:
|
|
structure: Nested structure, whose structure is given by nested lists,
|
|
tuples, and dicts. Note: numpy arrays and strings are considered
|
|
scalars.
|
|
flat_sequence: flat sequence to pack.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Returns:
|
|
packed: `flat_sequence` converted to have the same recursive structure as
|
|
`structure`.
|
|
|
|
Raises:
|
|
ValueError: If `flat_sequence` and `structure` have different
|
|
atom counts.
|
|
TypeError: `structure` is or contains a dict with non-sortable keys.
|
|
"""
|
|
return _pack_sequence_as(structure, flat_sequence, expand_composites)
|
|
|
|
|
|
@tf_export("nest.map_structure")
|
|
def map_structure(func, *structure, **kwargs):
|
|
"""Creates a new structure by applying `func` to each atom in `structure`.
|
|
|
|
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
|
|
for the definition of a structure.
|
|
|
|
Applies `func(x[0], x[1], ...)` where x[i] enumerates all atoms in
|
|
`structure[i]`. All items in `structure` must have the same arity,
|
|
and the return value will contain results with the same structure layout.
|
|
|
|
Examples:
|
|
|
|
* A single Python dict:
|
|
|
|
>>> a = {"hello": 24, "world": 76}
|
|
>>> tf.nest.map_structure(lambda p: p * 2, a)
|
|
{'hello': 48, 'world': 152}
|
|
|
|
* Multiple Python dictionaries:
|
|
|
|
>>> d1 = {"hello": 24, "world": 76}
|
|
>>> d2 = {"hello": 36, "world": 14}
|
|
>>> tf.nest.map_structure(lambda p1, p2: p1 + p2, d1, d2)
|
|
{'hello': 60, 'world': 90}
|
|
|
|
* A single Python list:
|
|
|
|
>>> a = [24, 76, "ab"]
|
|
>>> tf.nest.map_structure(lambda p: p * 2, a)
|
|
[48, 152, 'abab']
|
|
|
|
* Scalars:
|
|
|
|
>>> tf.nest.map_structure(lambda x, y: x + y, 3, 4)
|
|
7
|
|
|
|
* Empty structures:
|
|
|
|
>>> tf.nest.map_structure(lambda x: x + 1, ())
|
|
()
|
|
|
|
* Check the types of iterables:
|
|
|
|
>>> s1 = (((1, 2), 3), 4, (5, 6))
|
|
>>> s1_list = [[[1, 2], 3], 4, [5, 6]]
|
|
>>> tf.nest.map_structure(lambda x, y: None, s1, s1_list)
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: The two structures don't have the same nested structure
|
|
|
|
* Type check is set to False:
|
|
|
|
>>> s1 = (((1, 2), 3), 4, (5, 6))
|
|
>>> s1_list = [[[1, 2], 3], 4, [5, 6]]
|
|
>>> tf.nest.map_structure(lambda x, y: None, s1, s1_list, check_types=False)
|
|
(((None, None), None), None, (None, None))
|
|
|
|
Args:
|
|
func: A callable that accepts as many arguments as there are structures.
|
|
*structure: atom or nested structure.
|
|
**kwargs: Valid keyword args are:
|
|
* `check_types`: If set to `True` (default) the types of iterables within
|
|
the structures have to be same (e.g. `map_structure(func, [1], (1,))`
|
|
raises a `TypeError` exception). To allow this set this argument to
|
|
`False`. Note that namedtuples with identical name and fields are always
|
|
considered to have the same shallow structure.
|
|
* `expand_composites`: If set to `True`, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors. If `False` (the default), then composite tensors are
|
|
not expanded.
|
|
|
|
Returns:
|
|
A new structure with the same arity as `structure[0]`, whose atoms
|
|
correspond to `func(x[0], x[1], ...)` where `x[i]` is the atom in the
|
|
corresponding location in `structure[i]`. If there are different structure
|
|
types and `check_types` is `False` the structure types of the first
|
|
structure will be used.
|
|
|
|
Raises:
|
|
TypeError: If `func` is not callable or if the structures do not match
|
|
each other by depth tree.
|
|
ValueError: If no structure is provided or if the structures do not match
|
|
each other by type.
|
|
ValueError: If wrong keyword arguments are provided.
|
|
"""
|
|
if not callable(func):
|
|
raise TypeError("func must be callable, got: %s" % func)
|
|
|
|
if not structure:
|
|
raise ValueError("Must provide at least one structure")
|
|
|
|
check_types = kwargs.pop("check_types", True)
|
|
expand_composites = kwargs.pop("expand_composites", False)
|
|
|
|
if kwargs:
|
|
raise ValueError(
|
|
"Only valid keyword arguments are `check_types` and "
|
|
"`expand_composites`, not: `%s`" % ("`, `".join(kwargs.keys())))
|
|
|
|
for other in structure[1:]:
|
|
assert_same_structure(structure[0], other, check_types=check_types,
|
|
expand_composites=expand_composites)
|
|
|
|
flat_structure = (flatten(s, expand_composites) for s in structure)
|
|
entries = zip(*flat_structure)
|
|
|
|
return pack_sequence_as(
|
|
structure[0], [func(*x) for x in entries],
|
|
expand_composites=expand_composites)
|
|
|
|
|
|
def map_structure_with_paths(func, *structure, **kwargs):
|
|
"""Applies `func` to each entry in `structure` and returns a new structure.
|
|
|
|
Applies `func(path, x[0], x[1], ..., **kwargs)` where x[i] is an entry in
|
|
`structure[i]` and `path` is the common path to x[i] in the structures. All
|
|
structures in `structure` must have the same arity, and the return value will
|
|
contain the results with the same structure layout. Special kwarg
|
|
`check_types` determines whether the types of iterables within the structure
|
|
must be the same-- see **kwargs definition below.
|
|
|
|
Args:
|
|
func: A callable with the signature func(path, *values, **kwargs) that is
|
|
evaluated on the leaves of the structure.
|
|
*structure: A variable number of compatible structures to process.
|
|
**kwargs: Optional kwargs to be passed through to func. Special kwarg
|
|
`check_types` is not passed to func, but instead determines whether the
|
|
types of iterables within the structures have to be same (e.g.,
|
|
`map_structure(func, [1], (1,))` raises a `TypeError` exception). By
|
|
default, the types must match. To allow iteration over structures of
|
|
different types (but common arity), set this kwarg to `False`.
|
|
|
|
Returns:
|
|
A structure of the same form as the input structures whose leaves are the
|
|
result of evaluating func on corresponding leaves of the input structures.
|
|
|
|
Raises:
|
|
TypeError: If `func` is not callable or if the structures do not match
|
|
each other by depth tree.
|
|
TypeError: If `check_types` is not `False` and the two structures differ in
|
|
the type of sequence in any of their substructures.
|
|
ValueError: If no structures are provided.
|
|
"""
|
|
def wrapper_func(tuple_path, *inputs, **kwargs):
|
|
string_path = "/".join(str(s) for s in tuple_path)
|
|
return func(string_path, *inputs, **kwargs)
|
|
|
|
return map_structure_with_tuple_paths_up_to(structure[0],
|
|
wrapper_func,
|
|
*structure,
|
|
**kwargs)
|
|
|
|
|
|
def map_structure_with_tuple_paths(func, *structure, **kwargs):
|
|
"""Applies `func` to each entry in `structure` and returns a new structure.
|
|
|
|
Applies `func(tuple_path, x[0], x[1], ..., **kwargs)` where `x[i]` is an entry
|
|
in `structure[i]` and `tuple_path` is a tuple of indices and/or dictionary
|
|
keys (as returned by `nest.yield_flat_paths`), which uniquely specifies the
|
|
common path to x[i] in the structures. All structures in `structure` must have
|
|
the same arity, and the return value will contain the results in the same
|
|
structure. Special kwarg `check_types` determines whether the types of
|
|
iterables within the structure must be the same-- see **kwargs definition
|
|
below.
|
|
|
|
Args:
|
|
func: A callable with the signature `func(tuple_path, *values, **kwargs)`
|
|
that is evaluated on the leaves of the structure.
|
|
*structure: A variable number of compatible structures to process.
|
|
**kwargs: Optional kwargs to be passed through to func. Special kwarg
|
|
`check_types` is not passed to func, but instead determines whether the
|
|
types of iterables within the structures have to be same (e.g.
|
|
`map_structure(func, [1], (1,))` raises a `TypeError` exception). To allow
|
|
this set this argument to `False`.
|
|
|
|
Returns:
|
|
A structure of the same form as the input structures whose leaves are the
|
|
result of evaluating func on corresponding leaves of the input structures.
|
|
|
|
Raises:
|
|
TypeError: If `func` is not callable or if the structures do not match
|
|
each other by depth tree.
|
|
TypeError: If `check_types` is not `False` and the two structures differ in
|
|
the type of sequence in any of their substructures.
|
|
ValueError: If no structures are provided.
|
|
"""
|
|
return map_structure_with_tuple_paths_up_to(structure[0],
|
|
func,
|
|
*structure,
|
|
**kwargs)
|
|
|
|
|
|
def _yield_flat_up_to(shallow_tree, input_tree, is_nested_fn, path=()):
|
|
"""Yields (path, value) pairs of input_tree flattened up to shallow_tree.
|
|
|
|
Args:
|
|
shallow_tree: Nested structure. Traverse no further than its leaf nodes.
|
|
input_tree: Nested structure. Return the paths and values from this tree.
|
|
Must have the same upper structure as shallow_tree.
|
|
is_nested_fn: Function used to test if a value should be treated as a
|
|
nested structure.
|
|
path: Tuple. Optional argument, only used when recursing. The path from the
|
|
root of the original shallow_tree, down to the root of the shallow_tree
|
|
arg of this recursive call.
|
|
|
|
Yields:
|
|
Pairs of (path, value), where path the tuple path of a leaf node in
|
|
shallow_tree, and value is the value of the corresponding node in
|
|
input_tree.
|
|
"""
|
|
if not is_nested_fn(shallow_tree):
|
|
yield (path, input_tree)
|
|
else:
|
|
input_tree = dict(_yield_sorted_items(input_tree))
|
|
for shallow_key, shallow_subtree in _yield_sorted_items(shallow_tree):
|
|
subpath = path + (shallow_key,)
|
|
input_subtree = input_tree[shallow_key]
|
|
for leaf_path, leaf_value in _yield_flat_up_to(
|
|
shallow_subtree, input_subtree, is_nested_fn, path=subpath):
|
|
yield (leaf_path, leaf_value)
|
|
|
|
|
|
def assert_shallow_structure(shallow_tree,
|
|
input_tree,
|
|
check_types=True,
|
|
expand_composites=False):
|
|
"""Asserts that `shallow_tree` is a shallow structure of `input_tree`.
|
|
|
|
That is, this function tests if the `input_tree` structure can be created from
|
|
the `shallow_tree` structure by replacing its leaf nodes with deeper
|
|
tree structures.
|
|
|
|
Examples:
|
|
|
|
The following code will raise an exception:
|
|
```python
|
|
shallow_tree = {"a": "A", "b": "B"}
|
|
input_tree = {"a": 1, "c": 2}
|
|
assert_shallow_structure(shallow_tree, input_tree)
|
|
```
|
|
|
|
The following code will raise an exception:
|
|
```python
|
|
shallow_tree = ["a", "b"]
|
|
input_tree = ["c", ["d", "e"], "f"]
|
|
assert_shallow_structure(shallow_tree, input_tree)
|
|
```
|
|
|
|
Args:
|
|
shallow_tree: an arbitrarily nested structure.
|
|
input_tree: an arbitrarily nested structure.
|
|
check_types: if `True` (default) the sequence types of `shallow_tree` and
|
|
`input_tree` have to be the same. Note that even with check_types==True,
|
|
this function will consider two different namedtuple classes with the same
|
|
name and _fields attribute to be the same class.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
Raises:
|
|
TypeError: If `shallow_tree` is a sequence but `input_tree` is not.
|
|
TypeError: If the sequence types of `shallow_tree` are different from
|
|
`input_tree`. Only raised if `check_types` is `True`.
|
|
ValueError: If the sequence lengths of `shallow_tree` are different from
|
|
`input_tree`.
|
|
"""
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
if is_nested_fn(shallow_tree):
|
|
if not is_nested_fn(input_tree):
|
|
raise TypeError(
|
|
"If shallow structure is a sequence, input must also be a sequence. "
|
|
"Input has type: %s." % type(input_tree))
|
|
|
|
if isinstance(shallow_tree, _wrapt.ObjectProxy):
|
|
shallow_type = type(shallow_tree.__wrapped__)
|
|
else:
|
|
shallow_type = type(shallow_tree)
|
|
|
|
if check_types and not isinstance(input_tree, shallow_type):
|
|
# Duck-typing means that nest should be fine with two different
|
|
# namedtuples with identical name and fields.
|
|
shallow_is_namedtuple = is_namedtuple(shallow_tree, False)
|
|
input_is_namedtuple = is_namedtuple(input_tree, False)
|
|
if shallow_is_namedtuple and input_is_namedtuple:
|
|
if not same_namedtuples(shallow_tree, input_tree):
|
|
raise TypeError(_STRUCTURES_HAVE_MISMATCHING_TYPES.format(
|
|
input_type=type(input_tree),
|
|
shallow_type=type(shallow_tree)))
|
|
|
|
elif isinstance(shallow_tree, list) and isinstance(input_tree, list):
|
|
# List subclasses are considered the same,
|
|
# e.g. python list vs. _ListWrapper.
|
|
pass
|
|
|
|
elif ((_is_composite_tensor(shallow_tree) or
|
|
_is_type_spec(shallow_tree)) and
|
|
(_is_composite_tensor(input_tree) or _is_type_spec(input_tree))):
|
|
pass # Compatibility will be checked below.
|
|
|
|
elif not (isinstance(shallow_tree, _collections_abc.Mapping) and
|
|
isinstance(input_tree, _collections_abc.Mapping)):
|
|
raise TypeError(_STRUCTURES_HAVE_MISMATCHING_TYPES.format(
|
|
input_type=type(input_tree),
|
|
shallow_type=type(shallow_tree)))
|
|
|
|
if _is_composite_tensor(shallow_tree) or _is_composite_tensor(input_tree):
|
|
if not (
|
|
(_is_composite_tensor(input_tree) or _is_type_spec(input_tree)) and
|
|
(_is_composite_tensor(shallow_tree) or _is_type_spec(shallow_tree))):
|
|
raise TypeError(_STRUCTURES_HAVE_MISMATCHING_TYPES.format(
|
|
input_type=type(input_tree),
|
|
shallow_type=type(shallow_tree)))
|
|
# pylint: disable=protected-access
|
|
type_spec_1 = (shallow_tree if _is_type_spec(shallow_tree) else
|
|
shallow_tree._type_spec)._without_tensor_names()
|
|
type_spec_2 = (input_tree if _is_type_spec(input_tree) else
|
|
input_tree._type_spec)._without_tensor_names()
|
|
# TODO(b/246356867): Replace the most_specific_common_supertype below
|
|
# with get_structure.
|
|
if hasattr(type_spec_1, "_get_structure") and hasattr(type_spec_2,
|
|
"_get_structure"):
|
|
result = (type_spec_1._get_structure() == type_spec_2._get_structure()
|
|
or None)
|
|
else:
|
|
result = type_spec_1.most_specific_common_supertype([type_spec_2])
|
|
if result is None:
|
|
raise ValueError("Incompatible CompositeTensor TypeSpecs: %s vs. %s" %
|
|
(type_spec_1, type_spec_2))
|
|
# pylint: enable=protected-access
|
|
|
|
elif _is_type_spec(shallow_tree):
|
|
if not _is_type_spec(input_tree):
|
|
raise TypeError("If shallow structure is a TypeSpec, input must also "
|
|
"be a TypeSpec. Input has type: %s."
|
|
% type(input_tree))
|
|
else:
|
|
if len(input_tree) != len(shallow_tree):
|
|
raise ValueError(
|
|
_STRUCTURES_HAVE_MISMATCHING_LENGTHS.format(
|
|
input_length=len(input_tree), shallow_length=len(shallow_tree)))
|
|
elif len(input_tree) < len(shallow_tree):
|
|
raise ValueError(
|
|
_INPUT_TREE_SMALLER_THAN_SHALLOW_TREE.format(
|
|
input_size=len(input_tree), shallow_size=len(shallow_tree)))
|
|
|
|
if isinstance(shallow_tree, _collections_abc.Mapping):
|
|
absent_keys = set(shallow_tree) - set(input_tree)
|
|
if absent_keys:
|
|
raise ValueError(_SHALLOW_TREE_HAS_INVALID_KEYS
|
|
.format(sorted(absent_keys)))
|
|
|
|
for shallow_branch, input_branch in zip(_yield_value(shallow_tree),
|
|
_yield_value(input_tree)):
|
|
assert_shallow_structure(shallow_branch, input_branch,
|
|
check_types=check_types,
|
|
expand_composites=expand_composites)
|
|
|
|
|
|
@tf_export("__internal__.nest.flatten_up_to", v1=[])
|
|
def flatten_up_to(shallow_tree, input_tree, check_types=True,
|
|
expand_composites=False):
|
|
"""Flattens `input_tree` up to `shallow_tree`.
|
|
|
|
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
|
|
for the definition of a structure.
|
|
|
|
Any further depth in structure in `input_tree` is retained as structures in
|
|
the partially flatten output.
|
|
|
|
If `shallow_tree` and `input_tree` are atoms, this returns a
|
|
single-item list: `[input_tree]`.
|
|
|
|
Use Case:
|
|
|
|
Sometimes we may wish to partially flatten a structure, retaining some
|
|
of the nested structure. We achieve this by specifying a shallow structure,
|
|
`shallow_tree`, we wish to flatten up to.
|
|
|
|
The input, `input_tree`, can be thought of as having the same structure layout
|
|
as `shallow_tree`, but with leaf nodes that are themselves tree structures.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
input_tree = [[[2, 2], [3, 3]], [[4, 9], [5, 5]]]
|
|
shallow_tree = [[True, True], [False, True]]
|
|
|
|
flattened_input_tree = flatten_up_to(shallow_tree, input_tree)
|
|
flattened_shallow_tree = flatten_up_to(shallow_tree, shallow_tree)
|
|
|
|
# Output is:
|
|
# [[2, 2], [3, 3], [4, 9], [5, 5]]
|
|
# [True, True, False, True]
|
|
```
|
|
|
|
```python
|
|
input_tree = [[('a', 1), [('b', 2), [('c', 3), [('d', 4)]]]]]
|
|
shallow_tree = [['level_1', ['level_2', ['level_3', ['level_4']]]]]
|
|
|
|
input_tree_flattened_as_shallow_tree = flatten_up_to(shallow_tree, input_tree)
|
|
input_tree_flattened = flatten(input_tree)
|
|
|
|
# Output is:
|
|
# [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
|
|
# ['a', 1, 'b', 2, 'c', 3, 'd', 4]
|
|
```
|
|
|
|
Edge Cases for atoms:
|
|
|
|
```python
|
|
flatten_up_to(0, 0) # Output: [0]
|
|
flatten_up_to(0, [0, 1, 2]) # Output: [[0, 1, 2]]
|
|
flatten_up_to([0, 1, 2], 0) # Output: TypeError
|
|
flatten_up_to([0, 1, 2], [0, 1, 2]) # Output: [0, 1, 2]
|
|
```
|
|
|
|
Args:
|
|
shallow_tree: a possibly pruned structure of input_tree.
|
|
input_tree: an atom or a nested structure.
|
|
Note, numpy arrays are considered atoms.
|
|
check_types: bool. If True, check that each node in shallow_tree has the
|
|
same type as the corresponding node in input_tree.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Returns:
|
|
A Python list, the partially flattened version of `input_tree` according to
|
|
the structure of `shallow_tree`.
|
|
|
|
Raises:
|
|
TypeError: If `shallow_tree` is a nested structure but `input_tree` is not.
|
|
TypeError: If the structure types of `shallow_tree` are different from
|
|
`input_tree`.
|
|
ValueError: If the structure lengths of `shallow_tree` are different from
|
|
`input_tree`.
|
|
"""
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
assert_shallow_structure(shallow_tree,
|
|
input_tree,
|
|
check_types=check_types,
|
|
expand_composites=expand_composites)
|
|
# Discard paths returned by _yield_flat_up_to.
|
|
return [
|
|
v for _, v in _yield_flat_up_to(shallow_tree, input_tree, is_nested_fn)
|
|
]
|
|
|
|
|
|
def flatten_with_tuple_paths_up_to(shallow_tree,
|
|
input_tree,
|
|
check_types=True,
|
|
expand_composites=False):
|
|
"""Flattens `input_tree` up to `shallow_tree`.
|
|
|
|
Any further depth in structure in `input_tree` is retained as structures in
|
|
the partially flattened output.
|
|
|
|
Returns a list of (path, value) pairs, where value a leaf node in the
|
|
flattened tree, and path is the tuple path of that leaf in input_tree.
|
|
|
|
If `shallow_tree` and `input_tree` are not sequences, this returns a
|
|
single-item list: `[((), input_tree)]`.
|
|
|
|
Use Case:
|
|
|
|
Sometimes we may wish to partially flatten a nested sequence, retaining some
|
|
of the nested structure. We achieve this by specifying a shallow structure,
|
|
`shallow_tree`, we wish to flatten up to.
|
|
|
|
The input, `input_tree`, can be thought of as having the same structure layout
|
|
as `shallow_tree`, but with leaf nodes that are themselves tree structures.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
input_tree = [[[2, 2], [3, 3]], [[4, 9], [5, 5]]]
|
|
shallow_tree = [[True, True], [False, True]]
|
|
|
|
flattened_input_tree = flatten_with_tuple_paths_up_to(shallow_tree,
|
|
input_tree)
|
|
flattened_shallow_tree = flatten_with_tuple_paths_up_to(shallow_tree,
|
|
shallow_tree)
|
|
|
|
# Output is:
|
|
# [((0, 0), [2, 2]),
|
|
# ((0, 1), [3, 3]),
|
|
# ((1, 0), [4, 9]),
|
|
# ((1, 1), [5, 5])]
|
|
#
|
|
# [((0, 0), True),
|
|
# ((0, 1), True),
|
|
# ((1, 0), False),
|
|
# ((1, 1), True)]
|
|
```
|
|
|
|
```python
|
|
input_tree = [[('a', 1), [('b', 2), [('c', 3), [('d', 4)]]]]]
|
|
shallow_tree = [['level_1', ['level_2', ['level_3', ['level_4']]]]]
|
|
|
|
input_tree_flattened_as_shallow_tree = flatten_up_to(shallow_tree, input_tree)
|
|
input_tree_flattened = flatten(input_tree)
|
|
|
|
# Output is:
|
|
# [((0, 0), ('a', 1)),
|
|
# ((0, 1, 0), ('b', 2)),
|
|
# ((0, 1, 1, 0), ('c', 3)),
|
|
# ((0, 1, 1, 1), ('d', 4))]
|
|
# ['a', 1, 'b', 2, 'c', 3, 'd', 4]
|
|
```
|
|
|
|
Non-Sequence Edge Cases:
|
|
|
|
```python
|
|
flatten_with_tuple_paths_up_to(0, 0) # Output: [(), 0]
|
|
|
|
flatten_with_tuple_paths_up_to(0, [0, 1, 2]) # Output: [(), [0, 1, 2]]
|
|
|
|
flatten_with_tuple_paths_up_to([0, 1, 2], 0) # Output: TypeError
|
|
|
|
flatten_with_tuple_paths_up_to([0, 1, 2], [0, 1, 2])
|
|
# Output: [((0,) 0), ((1,), 1), ((2,), 2)]
|
|
```
|
|
|
|
Args:
|
|
shallow_tree: a possibly pruned structure of input_tree.
|
|
input_tree: an atom or a nested structure.
|
|
Note, numpy arrays are considered atoms.
|
|
check_types: bool. If True, check that each node in shallow_tree has the
|
|
same type as the corresponding node in input_tree.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Returns:
|
|
A Python list, the partially flattened version of `input_tree` according to
|
|
the structure of `shallow_tree`.
|
|
|
|
Raises:
|
|
TypeError: If `shallow_tree` is a nested structure but `input_tree` is not.
|
|
TypeError: If the structure types of `shallow_tree` are different from
|
|
`input_tree`.
|
|
ValueError: If the structure lengths of `shallow_tree` are different from
|
|
`input_tree`.
|
|
"""
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
assert_shallow_structure(shallow_tree,
|
|
input_tree,
|
|
check_types=check_types,
|
|
expand_composites=expand_composites)
|
|
return list(_yield_flat_up_to(shallow_tree, input_tree, is_nested_fn))
|
|
|
|
|
|
@tf_export("__internal__.nest.map_structure_up_to", v1=[])
|
|
def map_structure_up_to(shallow_tree, func, *inputs, **kwargs):
|
|
"""Applies a function or op to a number of partially flattened inputs.
|
|
|
|
The `inputs` are flattened up to `shallow_tree` before being mapped.
|
|
|
|
Use Case:
|
|
|
|
Sometimes we wish to apply a function to a partially flattened
|
|
structure (for example when the function itself takes structure inputs). We
|
|
achieve this by specifying a shallow structure, `shallow_tree` we wish to
|
|
flatten up to.
|
|
|
|
The `inputs`, can be thought of as having the same structure layout as
|
|
`shallow_tree`, but with leaf nodes that are themselves tree structures.
|
|
|
|
This function therefore will return something with the same base structure as
|
|
`shallow_tree`.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
shallow_tree = [None, None]
|
|
inp_val = [1, 2, 3]
|
|
out = map_structure_up_to(shallow_tree, lambda x: 2 * x, inp_val)
|
|
|
|
# Output is: [2, 4]
|
|
```
|
|
|
|
```python
|
|
ab_tuple = collections.namedtuple("ab_tuple", "a, b")
|
|
op_tuple = collections.namedtuple("op_tuple", "add, mul")
|
|
inp_val = ab_tuple(a=2, b=3)
|
|
inp_ops = ab_tuple(a=op_tuple(add=1, mul=2), b=op_tuple(add=2, mul=3))
|
|
out = map_structure_up_to(inp_val, lambda val, ops: (val + ops.add) * ops.mul,
|
|
inp_val, inp_ops)
|
|
|
|
# Output is: ab_tuple(a=6, b=15)
|
|
```
|
|
|
|
```python
|
|
data_list = [[2, 4, 6, 8], [[1, 3, 5, 7, 9], [3, 5, 7]]]
|
|
name_list = ['evens', ['odds', 'primes']]
|
|
out = map_structure_up_to(
|
|
name_list,
|
|
lambda name, sec: "first_{}_{}".format(len(sec), name),
|
|
name_list, data_list)
|
|
|
|
# Output is: ['first_4_evens', ['first_5_odds', 'first_3_primes']]
|
|
```
|
|
|
|
Args:
|
|
shallow_tree: a shallow structure, common to all the inputs.
|
|
func: callable which will be applied to each input individually.
|
|
*inputs: structures that are compatible with shallow_tree. The function
|
|
`func` is applied to corresponding structures due to partial flattening
|
|
of each input, so the function must support arity of `len(inputs)`.
|
|
**kwargs: kwargs to feed to func(). Special kwarg
|
|
`check_types` is not passed to func, but instead determines whether the
|
|
types of iterables within the structures have to be same (e.g.
|
|
`map_structure(func, [1], (1,))` raises a `TypeError` exception). To allow
|
|
this set this argument to `False`.
|
|
|
|
Raises:
|
|
TypeError: If `shallow_tree` is a nested structure but `input_tree` is not.
|
|
TypeError: If the structure types of `shallow_tree` are different from
|
|
`input_tree`.
|
|
ValueError: If the structure lengths of `shallow_tree` are different from
|
|
`input_tree`.
|
|
|
|
Returns:
|
|
result of repeatedly applying `func`, with the same structure layout as
|
|
`shallow_tree`.
|
|
"""
|
|
return map_structure_with_tuple_paths_up_to(
|
|
shallow_tree,
|
|
lambda _, *values: func(*values), # Discards the path arg.
|
|
*inputs,
|
|
**kwargs)
|
|
|
|
|
|
def map_structure_with_tuple_paths_up_to(shallow_tree, func, *inputs, **kwargs):
|
|
"""Applies a function or op to a number of partially flattened inputs.
|
|
|
|
Like map_structure_up_to(), except that the 'func' argument takes a path
|
|
tuple as its first argument, followed by the corresponding values from
|
|
*inputs.
|
|
|
|
Example:
|
|
|
|
```python
|
|
lowercase = {'a': 'a', 'b': ('b0', 'b1')}
|
|
uppercase = {'a': 'A', 'b': ('B0', 'B1')}
|
|
|
|
def print_path_and_values(path, *values):
|
|
print("path: {}, values: {}".format(path, values))
|
|
|
|
shallow_tree = {'a': None}
|
|
map_structure_with_tuple_paths_up_to(shallow_tree,
|
|
print_path_and_values,
|
|
lowercase,
|
|
uppercase)
|
|
path: ('a',), values: ('a', 'A')
|
|
path: ('b', 0), values: ('b0', 'B0')
|
|
path: ('b', 1), values: ('b1', 'B1')
|
|
|
|
shallow_tree = {'b': None}
|
|
map_structure_with_tuple_paths_up_to(shallow_tree,
|
|
print_path_and_values,
|
|
lowercase,
|
|
uppercase,
|
|
check_types=False)
|
|
path: ('b', 1), values: (('bo', 'b1'), ('B0', 'B1'))
|
|
|
|
shallow_tree = {'a': None, 'b': {1: None}}
|
|
map_structure_with_tuple_paths_up_to(shallow_tree,
|
|
print_path_and_values,
|
|
lowercase,
|
|
uppercase,
|
|
check_types=False)
|
|
path: ('a',), values: ('a', 'A')
|
|
path: ('b', 1), values: ('b1', B1')
|
|
```
|
|
|
|
Args:
|
|
shallow_tree: a shallow structure, common to all the inputs.
|
|
func: callable that takes args (path, inputs_0_value, ... , inputs_N_value),
|
|
where path is a tuple path to an atom in shallow_tree, and
|
|
inputs_i_value is the corresponding value from inputs[i].
|
|
*inputs: structures that are all structurally compatible with
|
|
shallow_tree.
|
|
**kwargs: kwargs to feed to func(). Special kwarg
|
|
`check_types` is not passed to func, but instead determines whether the
|
|
types of iterables within the structures have to be same (e.g.
|
|
`map_structure(func, [1], (1,))` raises a `TypeError` exception). To allow
|
|
this set this argument to `False`.
|
|
|
|
Raises:
|
|
TypeError: If `shallow_tree` is a nested structure but one of `*inputs` is
|
|
not.
|
|
TypeError: If the structure types of `shallow_tree` are different from
|
|
`input_tree`.
|
|
ValueError: If the structure lengths of `shallow_tree` are different from
|
|
`input_tree`.
|
|
|
|
Returns:
|
|
Result of repeatedly applying `func`. Has the same structure layout as
|
|
`shallow_tree`.
|
|
"""
|
|
if not inputs:
|
|
raise ValueError("Cannot map over no sequences")
|
|
|
|
check_types = kwargs.pop("check_types", True)
|
|
expand_composites = kwargs.pop("expand_composites", False)
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
|
|
for input_tree in inputs:
|
|
assert_shallow_structure(
|
|
shallow_tree,
|
|
input_tree,
|
|
check_types=check_types,
|
|
expand_composites=expand_composites)
|
|
|
|
# Flatten each input separately, apply the function to corresponding items,
|
|
# then repack based on the structure of the first input.
|
|
flat_value_gen = (
|
|
flatten_up_to( # pylint: disable=g-complex-comprehension
|
|
shallow_tree,
|
|
input_tree,
|
|
check_types,
|
|
expand_composites=expand_composites) for input_tree in inputs)
|
|
flat_path_gen = (
|
|
path
|
|
for path, _ in _yield_flat_up_to(shallow_tree, inputs[0], is_nested_fn))
|
|
results = [
|
|
func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
|
|
]
|
|
return pack_sequence_as(structure=shallow_tree, flat_sequence=results,
|
|
expand_composites=expand_composites)
|
|
|
|
|
|
@tf_export("__internal__.nest.get_traverse_shallow_structure", v1=[])
|
|
def get_traverse_shallow_structure(traverse_fn, structure,
|
|
expand_composites=False):
|
|
"""Generates a shallow structure from a `traverse_fn` and `structure`.
|
|
|
|
`traverse_fn` must accept any possible subtree of `structure` and return
|
|
a depth=1 structure containing `True` or `False` values, describing which
|
|
of the top-level subtrees may be traversed. It may also
|
|
return scalar `True` or `False` "traversal is OK / not OK for all subtrees."
|
|
|
|
Examples are available in the unit tests (nest_test.py).
|
|
|
|
Args:
|
|
traverse_fn: Function taking a substructure and returning either a scalar
|
|
`bool` (whether to traverse that substructure or not) or a depth=1
|
|
shallow structure of the same type, describing which parts of the
|
|
substructure to traverse.
|
|
structure: The structure to traverse.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Returns:
|
|
A shallow structure containing python bools, which can be passed to
|
|
`map_structure_up_to` and `flatten_up_to`.
|
|
|
|
Raises:
|
|
TypeError: if `traverse_fn` returns a nested structure for an atom input.
|
|
or a structure with depth higher than 1 for a nested structure input,
|
|
or if any leaf values in the returned structure or scalar are not type
|
|
`bool`.
|
|
"""
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
to_traverse = traverse_fn(structure)
|
|
if not is_nested_fn(structure):
|
|
if not isinstance(to_traverse, bool):
|
|
raise TypeError("traverse_fn returned structure: %s for non-structure: %s"
|
|
% (to_traverse, structure))
|
|
return to_traverse
|
|
level_traverse = []
|
|
if isinstance(to_traverse, bool):
|
|
if not to_traverse:
|
|
# Do not traverse this substructure at all. Exit early.
|
|
return False
|
|
else:
|
|
# Traverse the entire substructure.
|
|
for branch in _yield_value(structure):
|
|
level_traverse.append(
|
|
get_traverse_shallow_structure(traverse_fn, branch,
|
|
expand_composites=expand_composites))
|
|
elif not is_nested_fn(to_traverse):
|
|
raise TypeError("traverse_fn returned a non-bool scalar: %s for input: %s"
|
|
% (to_traverse, structure))
|
|
else:
|
|
# Traverse some subset of this substructure.
|
|
assert_shallow_structure(to_traverse, structure,
|
|
expand_composites=expand_composites)
|
|
for t, branch in zip(_yield_value(to_traverse),
|
|
_yield_value(structure)):
|
|
if not isinstance(t, bool):
|
|
raise TypeError(
|
|
"traverse_fn didn't return a depth=1 structure of bools. saw: %s "
|
|
" for structure: %s" % (to_traverse, structure))
|
|
if t:
|
|
level_traverse.append(
|
|
get_traverse_shallow_structure(traverse_fn, branch))
|
|
else:
|
|
level_traverse.append(False)
|
|
return _sequence_like(structure, level_traverse)
|
|
|
|
|
|
@tf_export("__internal__.nest.yield_flat_paths", v1=[])
|
|
def yield_flat_paths(nest, expand_composites=False):
|
|
"""Yields paths for some nested structure.
|
|
|
|
Refer to [tf.nest](https://www.tensorflow.org/api_docs/python/tf/nest)
|
|
for the definition of a structure.
|
|
|
|
Paths are lists of objects which can be str-converted, which may include
|
|
integers or other types which are used as indices in a dict.
|
|
|
|
The flat list will be in the corresponding order as if you called
|
|
`nest.flatten` on the structure. This is handy for naming Tensors such
|
|
the TF scope structure matches the tuple structure.
|
|
|
|
E.g. if we have a tuple `value = Foo(a=3, b=Bar(c=23, d=42))`
|
|
|
|
```shell
|
|
nest.flatten(value)
|
|
[3, 23, 42]
|
|
list(nest.yield_flat_paths(value))
|
|
[('a',), ('b', 'c'), ('b', 'd')]
|
|
```
|
|
|
|
```shell
|
|
list(nest.yield_flat_paths({'a': [3]}))
|
|
[('a', 0)]
|
|
list(nest.yield_flat_paths({'a': 3}))
|
|
[('a',)]
|
|
```
|
|
|
|
Args:
|
|
nest: the value to produce a flattened paths list for.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Yields:
|
|
Tuples containing index or key values which form the path to a specific
|
|
leaf value in the nested structure.
|
|
"""
|
|
is_nested_fn = _is_nested_or_composite if expand_composites else _is_nested
|
|
for k, _ in _yield_flat_up_to(nest, nest, is_nested_fn):
|
|
yield k
|
|
|
|
|
|
def flatten_with_joined_string_paths(structure, separator="/",
|
|
expand_composites=False):
|
|
"""Returns a list of (string path, atom) tuples.
|
|
|
|
The order of tuples produced matches that of `nest.flatten`. This allows you
|
|
to flatten a nested structure while keeping information about where in the
|
|
structure each atom was located. See `nest.yield_flat_paths`
|
|
for more information.
|
|
|
|
Args:
|
|
structure: the nested structure to flatten.
|
|
separator: string to separate levels of hierarchy in the results, defaults
|
|
to '/'.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Returns:
|
|
A list of (string, atom) tuples.
|
|
"""
|
|
flat_paths = yield_flat_paths(structure, expand_composites=expand_composites)
|
|
def stringify_and_join(path_elements):
|
|
return separator.join(str(path_element) for path_element in path_elements)
|
|
|
|
flat_string_paths = (stringify_and_join(path) for path in flat_paths)
|
|
return list(zip(flat_string_paths,
|
|
flatten(structure, expand_composites=expand_composites)))
|
|
|
|
|
|
def flatten_with_tuple_paths(structure, expand_composites=False):
|
|
"""Returns a list of `(tuple_path, atom)` tuples.
|
|
|
|
The order of pairs produced matches that of `nest.flatten`. This allows you
|
|
to flatten a nested structure while keeping information about where in the
|
|
structure each atom was located. See `nest.yield_flat_paths`
|
|
for more information about tuple paths.
|
|
|
|
Args:
|
|
structure: the nested structure to flatten.
|
|
expand_composites: If true, then composite tensors such as
|
|
`tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their
|
|
component tensors.
|
|
|
|
Returns:
|
|
A list of `(tuple_path, atom)` tuples. Each `tuple_path` is a tuple
|
|
of indices and/or dictionary keys that uniquely specify the path to
|
|
`atom` within `structure`.
|
|
"""
|
|
return list(zip(yield_flat_paths(structure,
|
|
expand_composites=expand_composites),
|
|
flatten(structure, expand_composites=expand_composites)))
|
|
|
|
|
|
@tf_export("__internal__.nest.list_to_tuple", v1=[])
|
|
def list_to_tuple(structure):
|
|
"""Replace all lists with tuples.
|
|
|
|
The fork of nest that tf.data uses treats lists as atoms, while
|
|
tf.nest treats them as structures to recurse into. Keras has chosen to adopt
|
|
the latter convention, and must therefore deeply replace all lists with tuples
|
|
before passing structures to Dataset.from_generator.
|
|
|
|
Args:
|
|
structure: A nested structure to be remapped.
|
|
|
|
Returns:
|
|
structure mapped to replace all lists with tuples.
|
|
"""
|
|
def sequence_fn(instance, args):
|
|
if isinstance(instance, list):
|
|
return tuple(args)
|
|
return _sequence_like(instance, args)
|
|
|
|
return _pack_sequence_as(structure, flatten(structure), False,
|
|
sequence_fn=sequence_fn)
|
|
|
|
|
|
_pywrap_utils.RegisterType("Mapping", _collections_abc.Mapping)
|
|
_pywrap_utils.RegisterType("MutableMapping", _collections_abc.MutableMapping)
|
|
_pywrap_utils.RegisterType("Sequence", _collections_abc.Sequence)
|
|
_pywrap_utils.RegisterType("MappingView", _collections_abc.MappingView)
|
|
_pywrap_utils.RegisterType("ObjectProxy", _wrapt.ObjectProxy)
|