wehub-resource-sync
306 lines
11 KiB
Python
306 lines
11 KiB
Python
# Copyright 2017 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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NOTE(mrry): This fork of the `tensorflow.python.util.nest` module
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makes two changes:
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1. It removes support for lists as a level of nesting in nested structures.
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2. It adds support for `SparseTensorValue` as an atomic element.
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The motivation for this change is twofold:
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1. It seems more natural for lists to be treated (e.g. in Dataset constructors)
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as tensors, rather than lists of (lists of...) tensors.
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2. This is needed because `SparseTensorValue` is implemented as a `namedtuple`
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that would normally be flattened and we want to be able to create sparse
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tensor from `SparseTensorValue's similarly to creating tensors from numpy
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arrays.
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"""
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from tensorflow.python.util import nest_util
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def is_nested(structure):
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return nest_util.is_nested(nest_util.Modality.DATA, structure)
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def flatten(structure):
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return nest_util.flatten(nest_util.Modality.DATA, structure)
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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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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 should be same as
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well. For dictionary, "type" of dictionary is considered to include its
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keys. In other words, two dictionaries with different keys are considered
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to have a different "type". If set to `False`, two iterables are
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considered same as long as they yield the elements that have same
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structures.
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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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nest_util.assert_same_structure(
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nest_util.Modality.DATA, nest1, nest2, check_types
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)
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def pack_sequence_as(structure, flat_sequence):
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"""Returns a given flattened sequence packed into a nest.
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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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Args:
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structure: tuple or list constructed of scalars and/or other tuples/lists,
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or a scalar. Note: numpy arrays are considered 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 nest and structure have different element counts.
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"""
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return nest_util.pack_sequence_as(
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nest_util.Modality.DATA, structure, flat_sequence, expand_composites=False
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)
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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 are 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 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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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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return nest_util.map_structure(
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nest_util.Modality.DATA, func, *structure, **check_types_dict
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)
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def assert_shallow_structure(shallow_tree, input_tree, check_types=True):
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"""Asserts that `shallow_tree` is a shallow structure of `input_tree`.
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That is, this function tests if the `input_tree` structure can be created from
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the `shallow_tree` structure by replacing its leaf nodes with deeper
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tree structures.
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Examples:
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The following code will raise an exception:
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```python
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shallow_tree = ["a", "b"]
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input_tree = ["c", ["d", "e"], "f"]
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assert_shallow_structure(shallow_tree, input_tree)
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```
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The following code will not raise an exception:
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```python
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shallow_tree = ["a", "b"]
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input_tree = ["c", ["d", "e"]]
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assert_shallow_structure(shallow_tree, input_tree)
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```
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Args:
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shallow_tree: an arbitrarily nested structure.
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input_tree: an arbitrarily nested structure.
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check_types: if `True` (default) the sequence types of `shallow_tree` and
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`input_tree` have to be the same.
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Raises:
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TypeError: If `shallow_tree` is a sequence but `input_tree` is not.
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TypeError: If the sequence types of `shallow_tree` are different from
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`input_tree`. Only raised if `check_types` is `True`.
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ValueError: If the sequence lengths of `shallow_tree` are different from
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`input_tree`.
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"""
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nest_util.assert_shallow_structure(
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nest_util.Modality.DATA, shallow_tree, input_tree, check_types
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)
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def flatten_up_to(shallow_tree, input_tree):
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"""Flattens `input_tree` up to `shallow_tree`.
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Any further depth in structure in `input_tree` is retained as elements in the
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partially flatten output.
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If `shallow_tree` and `input_tree` are not sequences, this returns a
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single-element list: `[input_tree]`.
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Use Case:
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Sometimes we may wish to partially flatten a nested sequence, retaining some
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of the nested structure. We achieve this by specifying a shallow structure,
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`shallow_tree`, we wish to flatten up to.
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The input, `input_tree`, can be thought of as having the same structure as
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`shallow_tree`, but with leaf nodes that are themselves tree structures.
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Examples:
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```python
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input_tree = [[[2, 2], [3, 3]], [[4, 9], [5, 5]]]
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shallow_tree = [[True, True], [False, True]]
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flattened_input_tree = flatten_up_to(shallow_tree, input_tree)
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flattened_shallow_tree = flatten_up_to(shallow_tree, shallow_tree)
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# Output is:
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# [[2, 2], [3, 3], [4, 9], [5, 5]]
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# [True, True, False, True]
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```
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```python
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input_tree = [[('a', 1), [('b', 2), [('c', 3), [('d', 4)]]]]]
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shallow_tree = [['level_1', ['level_2', ['level_3', ['level_4']]]]]
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input_tree_flattened_as_shallow_tree = flatten_up_to(shallow_tree, input_tree)
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input_tree_flattened = flatten(input_tree)
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# Output is:
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# [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
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# ['a', 1, 'b', 2, 'c', 3, 'd', 4]
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```
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Non-Sequence Edge Cases:
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```python
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flatten_up_to(0, 0) # Output: [0]
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flatten_up_to(0, [0, 1, 2]) # Output: [[0, 1, 2]]
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flatten_up_to([0, 1, 2], 0) # Output: TypeError
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flatten_up_to([0, 1, 2], [0, 1, 2]) # Output: [0, 1, 2]
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```
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Args:
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shallow_tree: a possibly pruned structure of input_tree.
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input_tree: an arbitrarily nested structure or a scalar object.
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Note, numpy arrays are considered scalars.
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Returns:
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A Python list, the partially flattened version of `input_tree` according to
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the structure of `shallow_tree`.
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Raises:
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TypeError: If `shallow_tree` is a sequence but `input_tree` is not.
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TypeError: If the sequence types of `shallow_tree` are different from
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`input_tree`.
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ValueError: If the sequence lengths of `shallow_tree` are different from
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`input_tree`.
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"""
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return nest_util.flatten_up_to(
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nest_util.Modality.DATA, shallow_tree, input_tree
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)
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def map_structure_up_to(shallow_tree, func, *inputs):
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"""Applies a function or op to a number of partially flattened inputs.
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The `inputs` are flattened up to `shallow_tree` before being mapped.
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Use Case:
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Sometimes we wish to apply a function to a partially flattened
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sequence (for example when the function itself takes sequence inputs). We
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achieve this by specifying a shallow structure, `shallow_tree` we wish to
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flatten up to.
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The `inputs`, can be thought of as having the same structure as
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`shallow_tree`, but with leaf nodes that are themselves tree structures.
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This function, therefore, will return something with the same base structure
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as `shallow_tree`.
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Examples:
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```python
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ab_tuple = collections.namedtuple("ab_tuple", "a, b")
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op_tuple = collections.namedtuple("op_tuple", "add, mul")
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inp_val = ab_tuple(a=2, b=3)
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inp_ops = ab_tuple(a=op_tuple(add=1, mul=2), b=op_tuple(add=2, mul=3))
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out = map_structure_up_to(inp_val, lambda val, ops: (val + ops.add) * ops.mul,
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inp_val, inp_ops)
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# Output is: ab_tuple(a=6, b=15)
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```
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```python
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data_list = [[2, 4, 6, 8], [[1, 3, 5, 7, 9], [3, 5, 7]]]
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name_list = ['evens', ['odds', 'primes']]
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out = map_structure_up_to(
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name_list,
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lambda name, sec: "first_{}_{}".format(len(sec), name),
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name_list, data_list)
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# Output is: ['first_4_evens', ['first_5_odds', 'first_3_primes']]
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```
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Args:
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shallow_tree: a shallow tree, common to all the inputs.
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func: callable which will be applied to each input individually.
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*inputs: arbitrarily nested combination of objects that are compatible with
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shallow_tree. The function `func` is applied to corresponding
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partially flattened elements of each input, so the function must support
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arity of `len(inputs)`.
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Raises:
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TypeError: If `shallow_tree` is a sequence but `input_tree` is not.
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TypeError: If the sequence types of `shallow_tree` are different from
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`input_tree`.
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ValueError: If the sequence lengths of `shallow_tree` are different from
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`input_tree`.
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Returns:
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result of repeatedly applying `func`, with same structure as
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`shallow_tree`.
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"""
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return nest_util.map_structure_up_to(
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nest_util.Modality.DATA, shallow_tree, func, *inputs
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)
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