763 lines
28 KiB
Python
763 lines
28 KiB
Python
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# Copyright 2016 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 for working with arbitrarily nested sequences of elements.
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This module can perform operations on nested structures. A nested structure is a
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Python sequence, tuple (including `namedtuple`), or dict that can contain
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further sequences, tuples, and dicts.
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The utilities here assume (and do not check) that the nested structures form a
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'tree', i.e., no references in the structure of the input of these functions
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should be recursive.
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Example structures: `((3, 4), 5, (6, 7, (9, 10), 8))`, `(np.array(0),
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(np.array([3, 4]), tf.constant([3, 4])))`
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections as _collections
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import six as _six
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from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
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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(_six.iterkeys(dict_))
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except TypeError:
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raise TypeError("nest only supports dicts with sortable keys.")
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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_tensorflow.IsNamedtuple(instance, strict)
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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`, or
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`collections.OrderedDict`.
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args: elements 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 isinstance(instance, dict):
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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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return type(instance)((key, result[key]) for key in _six.iterkeys(instance))
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elif _is_namedtuple(instance):
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return type(instance)(*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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if isinstance(iterable, dict):
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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 iterable[key]
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else:
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for value in iterable:
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yield value
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def is_sequence(seq):
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"""Returns a true if its input is a collections.Sequence (except strings).
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Args:
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seq: an input sequence.
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Returns:
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True if the sequence is a not a string and is a collections.Sequence or a
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dict.
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"""
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return _pywrap_tensorflow.IsSequence(seq)
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def flatten(nest):
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"""Returns a flat list from a given nested structure.
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If `nest` is not a sequence, tuple, or dict, then returns a single-element
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list: `[nest]`.
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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
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used instead. The same convention is followed in `pack_sequence_as`. This
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correctly repacks dicts and `OrderedDict`s after they have been flattened,
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and also allows flattening an `OrderedDict` and then repacking it back using
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a corresponding plain dict, or vice-versa.
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Dictionaries with non-sortable keys 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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Args:
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nest: an arbitrarily nested structure or a scalar object. Note, numpy
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arrays are considered scalars.
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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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return _pywrap_tensorflow.Flatten(nest)
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def _same_namedtuples(nest1, nest2):
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"""Returns True if the two namedtuples have the same name and fields."""
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return _pywrap_tensorflow.SameNamedtuples(nest1, nest2)
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def assert_same_structure(nest1, nest2, check_types=True):
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"""Asserts that two structures are nested in the same way.
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Note that namedtuples with identical name and fields are always considered
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to have the same shallow structure (even with `check_types=True`).
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For intance, this code will print `True`:
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```python
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def nt(a, b):
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return collections.namedtuple('foo', 'a b')(a, b)
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print(assert_same_structure(nt(0, 1), nt(2, 3)))
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```
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Args:
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nest1: an arbitrarily nested structure.
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nest2: an arbitrarily nested structure.
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check_types: if `True` (default) types of sequences are checked as well,
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including the keys of dictionaries. If set to `False`, for example a
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list and a tuple of objects will look the same if they have the same
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size. Note that namedtuples with identical name and fields are always
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considered to have the same shallow structure. Two types will also be
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considered the same if they are both list subtypes (which allows "list"
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and "_ListWrapper" from checkpointable dependency tracking to compare
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equal).
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Raises:
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ValueError: If the two structures do not have the same number of elements or
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if the two structures are not nested in the same way.
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TypeError: If the two structures differ in the type of sequence in any of
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their substructures. Only possible if `check_types` is `True`.
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"""
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_pywrap_tensorflow.AssertSameStructure(nest1, nest2, check_types)
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def flatten_dict_items(dictionary):
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"""Returns a dictionary with flattened keys and values.
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This function flattens the keys and values of a dictionary, which can be
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arbitrarily nested structures, and returns the flattened version of such
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structures:
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```python
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example_dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", "d"))}
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result = {4: "a", 5: "b", 6: "c", 8: "d"}
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flatten_dict_items(example_dictionary) == result
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```
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The input dictionary must satisfy two properties:
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1. Its keys and values should have the same exact nested structure.
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2. The set of all flattened keys of the dictionary must not contain repeated
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keys.
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Args:
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dictionary: the dictionary to zip
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Returns:
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The zipped dictionary.
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Raises:
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TypeError: If the input is not a dictionary.
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ValueError: If any key and value have not the same structure, or if keys are
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not unique.
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"""
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if not isinstance(dictionary, dict):
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raise TypeError("input must be a dictionary")
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flat_dictionary = {}
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for i, v in _six.iteritems(dictionary):
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if not is_sequence(i):
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if i in flat_dictionary:
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raise ValueError(
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"Could not flatten dictionary: key %s is not unique." % i)
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flat_dictionary[i] = v
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else:
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flat_i = flatten(i)
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flat_v = flatten(v)
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if len(flat_i) != len(flat_v):
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raise ValueError(
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"Could not flatten dictionary. Key had %d elements, but value had "
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"%d elements. Key: %s, value: %s."
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% (len(flat_i), len(flat_v), flat_i, flat_v))
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for new_i, new_v in zip(flat_i, flat_v):
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if new_i in flat_dictionary:
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raise ValueError(
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"Could not flatten dictionary: key %s is not unique."
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% (new_i))
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flat_dictionary[new_i] = new_v
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return flat_dictionary
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def _packed_nest_with_indices(structure, flat, index):
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"""Helper function for pack_sequence_as.
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Args:
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structure: Substructure (list / tuple / dict) to mimic.
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flat: Flattened values to output substructure for.
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index: Index at which to start reading from flat.
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Returns:
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The tuple (new_index, child), where:
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* new_index - the updated index into `flat` having processed `structure`.
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* packed - the subset of `flat` corresponding to `structure`,
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having started at `index`, and packed into the same nested
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format.
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Raises:
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ValueError: if `structure` contains more elements than `flat`
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(assuming indexing starts from `index`).
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"""
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packed = []
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for s in _yield_value(structure):
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if is_sequence(s):
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new_index, child = _packed_nest_with_indices(s, flat, index)
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packed.append(_sequence_like(s, child))
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index = new_index
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else:
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packed.append(flat[index])
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index += 1
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return index, packed
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def pack_sequence_as(structure, flat_sequence):
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"""Returns a given flattened sequence packed into a given structure.
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If `structure` is a scalar, `flat_sequence` must be a single-element list;
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in this case the return value is `flat_sequence[0]`.
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If `structure` is or contains a dict instance, the keys will be sorted to
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pack the flat sequence in deterministic order. This is true also for
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`OrderedDict` instances: their sequence order is ignored, the sorting order of
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keys is used instead. The same convention is followed in `flatten`.
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This correctly repacks dicts and `OrderedDict`s after they have been
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flattened, and also allows flattening an `OrderedDict` and then repacking it
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back using a corresponding plain dict, or vice-versa.
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Dictionaries with non-sortable keys cannot be flattened.
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Args:
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structure: Nested structure, whose structure is given by nested lists,
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tuples, and dicts. Note: numpy arrays and strings are considered
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scalars.
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flat_sequence: flat sequence to pack.
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Returns:
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packed: `flat_sequence` converted to have the same recursive structure as
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`structure`.
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Raises:
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ValueError: If `flat_sequence` and `structure` have different
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element counts.
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TypeError: `structure` is or contains a dict with non-sortable keys.
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"""
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if not is_sequence(flat_sequence):
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raise TypeError("flat_sequence must be a sequence")
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if not is_sequence(structure):
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if len(flat_sequence) != 1:
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raise ValueError("Structure is a scalar but len(flat_sequence) == %d > 1"
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% len(flat_sequence))
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return flat_sequence[0]
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flat_structure = flatten(structure)
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if len(flat_structure) != len(flat_sequence):
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raise ValueError(
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"Could not pack sequence. Structure had %d elements, but flat_sequence "
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"had %d elements. Structure: %s, flat_sequence: %s."
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% (len(flat_structure), len(flat_sequence), structure, flat_sequence))
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_, packed = _packed_nest_with_indices(structure, flat_sequence, 0)
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return _sequence_like(structure, packed)
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def map_structure(func, *structure, **check_types_dict):
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"""Applies `func` to each entry in `structure` and returns a new structure.
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Applies `func(x[0], x[1], ...)` where x[i] is an entry in
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`structure[i]`. All structures in `structure` must have the same arity,
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and the return value will contain the results in the same structure.
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Args:
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func: A callable that accepts as many arguments as there are structures.
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*structure: scalar, or tuple or list of constructed scalars and/or other
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tuples/lists, or scalars. Note: numpy arrays are considered as scalars.
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**check_types_dict: only valid keyword argument is `check_types`. If set to
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`True` (default) the types of iterables within the structures have to be
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same (e.g. `map_structure(func, [1], (1,))` raises a `TypeError`
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exception). To allow this set this argument to `False`.
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Note that namedtuples with identical name and fields are always
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considered to have the same shallow structure.
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Returns:
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A new structure with the same arity as `structure`, whose values correspond
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to `func(x[0], x[1], ...)` where `x[i]` is a value in the corresponding
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location in `structure[i]`. If there are different sequence types and
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`check_types` is `False` the sequence types of the first structure will be
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used.
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Raises:
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TypeError: If `func` is not callable or if the structures do not match
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each other by depth tree.
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ValueError: If no structure is provided or if the structures do not match
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each other by type.
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ValueError: If wrong keyword arguments are provided.
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"""
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if not callable(func):
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raise TypeError("func must be callable, got: %s" % func)
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if not structure:
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raise ValueError("Must provide at least one structure")
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if check_types_dict:
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if "check_types" not in check_types_dict or len(check_types_dict) > 1:
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raise ValueError("Only valid keyword argument is check_types")
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check_types = check_types_dict["check_types"]
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else:
|
||
|
|
check_types = True
|
||
|
|
|
||
|
|
for other in structure[1:]:
|
||
|
|
assert_same_structure(structure[0], other, check_types=check_types)
|
||
|
|
|
||
|
|
flat_structure = [flatten(s) for s in structure]
|
||
|
|
entries = zip(*flat_structure)
|
||
|
|
|
||
|
|
return pack_sequence_as(
|
||
|
|
structure[0], [func(*x) for x in entries])
|
||
|
|
|
||
|
|
|
||
|
|
def _yield_flat_up_to(shallow_tree, input_tree):
|
||
|
|
"""Yields elements `input_tree` partially flattened up to `shallow_tree`."""
|
||
|
|
if is_sequence(shallow_tree):
|
||
|
|
for shallow_branch, input_branch in zip(_yield_value(shallow_tree),
|
||
|
|
_yield_value(input_tree)):
|
||
|
|
for input_leaf in _yield_flat_up_to(shallow_branch, input_branch):
|
||
|
|
yield input_leaf
|
||
|
|
else:
|
||
|
|
yield input_tree
|
||
|
|
|
||
|
|
|
||
|
|
def assert_shallow_structure(shallow_tree, input_tree, check_types=True):
|
||
|
|
"""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", "b"]
|
||
|
|
input_tree = ["c", ["d", "e"], "f"]
|
||
|
|
assert_shallow_structure(shallow_tree, input_tree)
|
||
|
|
```
|
||
|
|
|
||
|
|
The following code will not raise an exception:
|
||
|
|
```python
|
||
|
|
shallow_tree = ["a", "b"]
|
||
|
|
input_tree = ["c", ["d", "e"]]
|
||
|
|
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.
|
||
|
|
|
||
|
|
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`.
|
||
|
|
"""
|
||
|
|
if is_sequence(shallow_tree):
|
||
|
|
if not is_sequence(input_tree):
|
||
|
|
raise TypeError(
|
||
|
|
"If shallow structure is a sequence, input must also be a sequence. "
|
||
|
|
"Input has type: %s." % type(input_tree))
|
||
|
|
|
||
|
|
if check_types and not isinstance(input_tree, type(shallow_tree)):
|
||
|
|
# 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(
|
||
|
|
"The two namedtuples don't have the same sequence type. Input "
|
||
|
|
"structure has type %s, while shallow structure has type %s."
|
||
|
|
% (type(input_tree), type(shallow_tree)))
|
||
|
|
else:
|
||
|
|
raise TypeError(
|
||
|
|
"The two structures don't have the same sequence type. Input "
|
||
|
|
"structure has type %s, while shallow structure has type %s."
|
||
|
|
% (type(input_tree), type(shallow_tree)))
|
||
|
|
|
||
|
|
if len(input_tree) != len(shallow_tree):
|
||
|
|
raise ValueError(
|
||
|
|
"The two structures don't have the same sequence length. Input "
|
||
|
|
"structure has length %s, while shallow structure has length %s."
|
||
|
|
% (len(input_tree), len(shallow_tree)))
|
||
|
|
|
||
|
|
if check_types and isinstance(shallow_tree, dict):
|
||
|
|
if set(input_tree) != set(shallow_tree):
|
||
|
|
raise ValueError(
|
||
|
|
"The two structures don't have the same keys. Input "
|
||
|
|
"structure has keys %s, while shallow structure has keys %s." %
|
||
|
|
(list(_six.iterkeys(input_tree)),
|
||
|
|
list(_six.iterkeys(shallow_tree))))
|
||
|
|
|
||
|
|
input_tree = list(sorted(_six.iteritems(input_tree)))
|
||
|
|
shallow_tree = list(sorted(_six.iteritems(shallow_tree)))
|
||
|
|
|
||
|
|
for shallow_branch, input_branch in zip(shallow_tree, input_tree):
|
||
|
|
assert_shallow_structure(shallow_branch, input_branch,
|
||
|
|
check_types=check_types)
|
||
|
|
|
||
|
|
|
||
|
|
def flatten_up_to(shallow_tree, input_tree):
|
||
|
|
"""Flattens `input_tree` up to `shallow_tree`.
|
||
|
|
|
||
|
|
Any further depth in structure in `input_tree` is retained as elements in the
|
||
|
|
partially flatten output.
|
||
|
|
|
||
|
|
If `shallow_tree` and `input_tree` are not sequences, this returns a
|
||
|
|
single-element 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 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]
|
||
|
|
```
|
||
|
|
|
||
|
|
Non-Sequence Edge Cases:
|
||
|
|
|
||
|
|
```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 arbitrarily nested structure or a scalar object.
|
||
|
|
Note, numpy arrays are considered scalars.
|
||
|
|
|
||
|
|
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 sequence but `input_tree` is not.
|
||
|
|
TypeError: If the sequence types of `shallow_tree` are different from
|
||
|
|
`input_tree`.
|
||
|
|
ValueError: If the sequence lengths of `shallow_tree` are different from
|
||
|
|
`input_tree`.
|
||
|
|
"""
|
||
|
|
assert_shallow_structure(shallow_tree, input_tree)
|
||
|
|
return list(_yield_flat_up_to(shallow_tree, input_tree))
|
||
|
|
|
||
|
|
|
||
|
|
def map_structure_up_to(shallow_tree, func, *inputs):
|
||
|
|
"""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
|
||
|
|
sequence (for example when the function itself takes sequence 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 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
|
||
|
|
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 tree, common to all the inputs.
|
||
|
|
func: callable which will be applied to each input individually.
|
||
|
|
*inputs: arbitrarily nested combination of objects that are compatible with
|
||
|
|
shallow_tree. The function `func` is applied to corresponding
|
||
|
|
partially flattened elements of each input, so the function must support
|
||
|
|
arity of `len(inputs)`.
|
||
|
|
|
||
|
|
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`.
|
||
|
|
ValueError: If the sequence lengths of `shallow_tree` are different from
|
||
|
|
`input_tree`.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
result of repeatedly applying `func`, with same structure as
|
||
|
|
`shallow_tree`.
|
||
|
|
"""
|
||
|
|
if not inputs:
|
||
|
|
raise ValueError("Cannot map over no sequences")
|
||
|
|
for input_tree in inputs:
|
||
|
|
assert_shallow_structure(shallow_tree, input_tree)
|
||
|
|
|
||
|
|
# Flatten each input separately, apply the function to corresponding elements,
|
||
|
|
# then repack based on the structure of the first input.
|
||
|
|
all_flattened_up_to = [flatten_up_to(shallow_tree, input_tree)
|
||
|
|
for input_tree in inputs]
|
||
|
|
results = [func(*tensors) for tensors in zip(*all_flattened_up_to)]
|
||
|
|
return pack_sequence_as(structure=shallow_tree, flat_sequence=results)
|
||
|
|
|
||
|
|
|
||
|
|
def get_traverse_shallow_structure(traverse_fn, structure):
|
||
|
|
"""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.
|
||
|
|
|
||
|
|
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 sequence for a non-sequence input,
|
||
|
|
or a structure with depth higher than 1 for a sequence input,
|
||
|
|
or if any leaf values in the returned structure or scalar are not type
|
||
|
|
`bool`.
|
||
|
|
"""
|
||
|
|
to_traverse = traverse_fn(structure)
|
||
|
|
if not is_sequence(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))
|
||
|
|
elif not is_sequence(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)
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
def yield_flat_paths(nest):
|
||
|
|
"""Yields paths for some nested 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
|
||
|
|
`snt.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.
|
||
|
|
|
||
|
|
Yields:
|
||
|
|
Tuples containing index or key values which form the path to a specific
|
||
|
|
leaf value in the nested structure.
|
||
|
|
"""
|
||
|
|
|
||
|
|
# The _maybe_add_final_path_element function is used below in order to avoid
|
||
|
|
# adding trailing slashes when the sub-element recursed into is a leaf.
|
||
|
|
if isinstance(nest, dict):
|
||
|
|
for key in _sorted(nest):
|
||
|
|
value = nest[key]
|
||
|
|
for sub_path in yield_flat_paths(value):
|
||
|
|
yield (key,) + sub_path
|
||
|
|
elif _is_namedtuple(nest):
|
||
|
|
for key in nest._fields:
|
||
|
|
value = getattr(nest, key)
|
||
|
|
for sub_path in yield_flat_paths(value):
|
||
|
|
yield (key,) + sub_path
|
||
|
|
elif isinstance(nest, _six.string_types):
|
||
|
|
yield ()
|
||
|
|
elif isinstance(nest, _collections.Sequence):
|
||
|
|
for idx, value in enumerate(nest):
|
||
|
|
for sub_path in yield_flat_paths(value):
|
||
|
|
yield (idx,) + sub_path
|
||
|
|
else:
|
||
|
|
yield ()
|
||
|
|
|
||
|
|
|
||
|
|
def flatten_with_joined_string_paths(structure, separator="/"):
|
||
|
|
"""Returns a list of (string path, data element) 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 data element 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 '/'.
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A list of (string, data element) tuples.
|
||
|
|
"""
|
||
|
|
flat_paths = yield_flat_paths(structure)
|
||
|
|
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)))
|
||
|
|
|
||
|
|
|
||
|
|
_pywrap_tensorflow.RegisterSequenceClass(_collections.Sequence)
|