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655 lines (531 loc) · 24.2 KB
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# -*- coding: utf-8 -*-
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Operations for working with string Tensors.
API docstring: tensorflow.strings
"""
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_parsing_ops
from tensorflow.python.ops import gen_string_ops
from tensorflow.python.ops import math_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
# pylint: disable=g-bad-import-order
from tensorflow.python.ops.gen_string_ops import *
from tensorflow.python.util import compat as util_compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import dispatch
from tensorflow.python.util.tf_export import tf_export
# pylint: enable=g-bad-import-order
# pylint: enable=wildcard-import
# pylint: disable=redefined-builtin
@tf_export("strings.regex_full_match")
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def regex_full_match(input, pattern, name=None):
r"""Match elements of `input` with regex `pattern`.
Args:
input: string `Tensor`, the source strings to process.
pattern: string or scalar string `Tensor`, regular expression to use,
see more details at https://github.com/google/re2/wiki/Syntax
name: Name of the op.
Returns:
bool `Tensor` of the same shape as `input` with match results.
"""
if isinstance(pattern, util_compat.bytes_or_text_types):
# When `pattern` is static through the life of the op we can
# use a version which performs the expensive regex compilation once at
# creation time.
return gen_string_ops.static_regex_full_match(
input=input, pattern=pattern, name=name)
return gen_string_ops.regex_full_match(
input=input, pattern=pattern, name=name)
regex_full_match.__doc__ = gen_string_ops.regex_full_match.__doc__
@tf_export(
"strings.regex_replace", v1=["strings.regex_replace", "regex_replace"])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints("regex_replace")
def regex_replace(input, pattern, rewrite, replace_global=True, name=None):
r"""Replace elements of `input` matching regex `pattern` with `rewrite`.
>>> tf.strings.regex_replace("Text with tags.<br /><b>contains html</b>",
... "<[^>]+>", " ")
<tf.Tensor: shape=(), dtype=string, numpy=b'Text with tags. contains html '>
Args:
input: string `Tensor`, the source strings to process.
pattern: string or scalar string `Tensor`, regular expression to use,
see more details at https://github.com/google/re2/wiki/Syntax
rewrite: string or scalar string `Tensor`, value to use in match
replacement, supports backslash-escaped digits (\1 to \9) can be to insert
text matching corresponding parenthesized group.
replace_global: `bool`, if `True` replace all non-overlapping matches,
else replace only the first match.
name: A name for the operation (optional).
Returns:
string `Tensor` of the same shape as `input` with specified replacements.
"""
if (isinstance(pattern, util_compat.bytes_or_text_types) and
isinstance(rewrite, util_compat.bytes_or_text_types)):
# When `pattern` and `rewrite` are static through the life of the op we can
# use a version which performs the expensive regex compilation once at
# creation time.
return gen_string_ops.static_regex_replace(
input=input, pattern=pattern,
rewrite=rewrite, replace_global=replace_global,
name=name)
return gen_string_ops.regex_replace(
input=input, pattern=pattern,
rewrite=rewrite, replace_global=replace_global,
name=name)
@tf_export("strings.format")
@dispatch.add_dispatch_support
def string_format(template, inputs, placeholder="{}", summarize=3, name=None):
r"""Formats a string template using a list of tensors.
Formats a string template using a list of tensors, abbreviating tensors by
only printing the first and last `summarize` elements of each dimension
(recursively). If formatting only one tensor into a template, the tensor does
not have to be wrapped in a list.
Example:
Formatting a single-tensor template:
>>> tensor = tf.range(5)
>>> tf.strings.format("tensor: {}, suffix", tensor)
<tf.Tensor: shape=(), dtype=string, numpy=b'tensor: [0 1 2 3 4], suffix'>
Formatting a multi-tensor template:
>>> tensor_a = tf.range(2)
>>> tensor_b = tf.range(1, 4, 2)
>>> tf.strings.format("a: {}, b: {}, suffix", (tensor_a, tensor_b))
<tf.Tensor: shape=(), dtype=string, numpy=b'a: [0 1], b: [1 3], suffix'>
Args:
template: A string template to format tensor values into.
inputs: A list of `Tensor` objects, or a single Tensor.
The list of tensors to format into the template string. If a solitary
tensor is passed in, the input tensor will automatically be wrapped as a
list.
placeholder: An optional `string`. Defaults to `{}`.
At each placeholder occurring in the template, a subsequent tensor
will be inserted.
summarize: An optional `int`. Defaults to `3`.
When formatting the tensors, show the first and last `summarize`
entries of each tensor dimension (recursively). If set to -1, all
elements of the tensor will be shown.
name: A name for the operation (optional).
Returns:
A scalar `Tensor` of type `string`.
Raises:
ValueError: if the number of placeholders does not match the number of
inputs.
"""
# If there is only one tensor to format, we will automatically wrap it in a
# list to simplify the user experience
if tensor_util.is_tf_type(inputs):
inputs = [inputs]
if template.count(placeholder) != len(inputs):
raise ValueError(f"The template expects {template.count(placeholder)} "
f"tensors, but the inputs only has {len(inputs)}. "
"Please ensure the number of placeholders in template "
"matches inputs length.")
return gen_string_ops.string_format(inputs,
template=template,
placeholder=placeholder,
summarize=summarize,
name=name)
# Note: tf.strings.split is exported in ragged/ragged_string_ops.py, which
# defines a wrapper for this function.
def string_split(source, sep=None, skip_empty=True, delimiter=None): # pylint: disable=invalid-name
"""Split elements of `source` based on `delimiter` into a `SparseTensor`.
Let N be the size of source (typically N will be the batch size). Split each
element of `source` based on `delimiter` and return a `SparseTensor`
containing the split tokens. Empty tokens are ignored.
If `sep` is an empty string, each element of the `source` is split
into individual strings, each containing one byte. (This includes splitting
multibyte sequences of UTF-8.) If delimiter contains multiple bytes, it is
treated as a set of delimiters with each considered a potential split point.
For example:
N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output
will be
st.indices = [0, 0;
0, 1;
1, 0;
1, 1;
1, 2]
st.shape = [2, 3]
st.values = ['hello', 'world', 'a', 'b', 'c']
Args:
source: `1-D` string `Tensor`, the strings to split.
sep: `0-D` string `Tensor`, the delimiter character, the string should
be length 0 or 1. Default is ' '.
skip_empty: A `bool`. If `True`, skip the empty strings from the result.
delimiter: deprecated alias for `sep`.
Raises:
ValueError: If delimiter is not a string.
Returns:
A `SparseTensor` of rank `2`, the strings split according to the delimiter.
The first column of the indices corresponds to the row in `source` and the
second column corresponds to the index of the split component in this row.
"""
delimiter = deprecation.deprecated_argument_lookup(
"sep", sep, "delimiter", delimiter)
if delimiter is None:
delimiter = " "
delimiter = ops.convert_to_tensor(delimiter, dtype=dtypes.string)
source = ops.convert_to_tensor(source, dtype=dtypes.string)
indices, values, shape = gen_string_ops.string_split(
source, delimiter=delimiter, skip_empty=skip_empty)
indices.set_shape([None, 2])
values.set_shape([None])
shape.set_shape([2])
return sparse_tensor.SparseTensor(indices, values, shape)
# Note: tf.strings.split is exported in ragged/ragged_string_ops.py, which
# defines a wrapper for this function.
def string_split_v2(source, sep=None, maxsplit=-1):
"""Split elements of `source` based on `sep` into a `SparseTensor`.
Let N be the size of source (typically N will be the batch size). Split each
element of `source` based on `sep` and return a `SparseTensor`
containing the split tokens. Empty tokens are ignored.
For example, N = 2, source[0] is 'hello world' and source[1] is 'a b c',
then the output will be
st.indices = [0, 0;
0, 1;
1, 0;
1, 1;
1, 2]
st.shape = [2, 3]
st.values = ['hello', 'world', 'a', 'b', 'c']
If `sep` is given, consecutive delimiters are not grouped together and are
deemed to delimit empty strings. For example, source of `"1<>2<><>3"` and
sep of `"<>"` returns `["1", "2", "", "3"]`. If `sep` is None or an empty
string, consecutive whitespace are regarded as a single separator, and the
result will contain no empty strings at the start or end if the string has
leading or trailing whitespace.
Note that the above mentioned behavior matches python's str.split.
Args:
source: `1-D` string `Tensor`, the strings to split.
sep: `0-D` string `Tensor`, the delimiter character.
maxsplit: An `int`. If `maxsplit > 0`, limit of the split of the result.
Raises:
ValueError: If sep is not a string.
Returns:
A `SparseTensor` of rank `2`, the strings split according to the delimiter.
The first column of the indices corresponds to the row in `source` and the
second column corresponds to the index of the split component in this row.
"""
if sep is None:
sep = ""
sep = ops.convert_to_tensor(sep, dtype=dtypes.string)
source = ops.convert_to_tensor(source, dtype=dtypes.string)
indices, values, shape = gen_string_ops.string_split_v2(
source, sep=sep, maxsplit=maxsplit)
indices.set_shape([None, 2])
values.set_shape([None])
shape.set_shape([2])
return sparse_tensor.SparseTensor(indices, values, shape)
def _reduce_join_reduction_dims(x, axis):
"""Returns range(rank(x) - 1, 0, -1) if axis is None; or axis otherwise."""
if axis is not None:
return axis
else:
# Fast path: avoid creating Rank and Range ops if ndims is known.
if x.get_shape().ndims is not None:
return constant_op.constant(
np.arange(x.get_shape().ndims - 1, -1, -1), dtype=dtypes.int32)
# Otherwise, we rely on Range and Rank to do the right thing at run-time.
return math_ops.range(array_ops.rank(x) - 1, -1, -1)
@tf_export(v1=["strings.reduce_join", "reduce_join"])
@dispatch.add_dispatch_support
@deprecation.deprecated_args(None,
"keep_dims is deprecated, use keepdims instead",
"keep_dims")
@deprecation.deprecated_endpoints("reduce_join")
def reduce_join(inputs, axis=None, # pylint: disable=missing-docstring
keep_dims=None,
separator="",
name=None,
reduction_indices=None,
keepdims=None):
keepdims = deprecation.deprecated_argument_lookup("keepdims", keepdims,
"keep_dims", keep_dims)
if keep_dims is None:
keep_dims = False
axis = deprecation.deprecated_argument_lookup("axis", axis,
"reduction_indices",
reduction_indices)
return reduce_join_v2(
inputs=inputs,
axis=axis,
keepdims=keepdims,
separator=separator,
name=name)
@tf_export("strings.reduce_join", v1=[])
@dispatch.add_dispatch_support
def reduce_join_v2( # pylint: disable=missing-docstring
inputs,
axis=None,
keepdims=False,
separator="",
name=None):
"""Joins all strings into a single string, or joins along an axis.
This is the reduction operation for the elementwise `tf.strings.join` op.
>>> tf.strings.reduce_join([['abc','123'],
... ['def','456']]).numpy()
b'abc123def456'
>>> tf.strings.reduce_join([['abc','123'],
... ['def','456']], axis=-1).numpy()
array([b'abc123', b'def456'], dtype=object)
>>> tf.strings.reduce_join([['abc','123'],
... ['def','456']],
... axis=-1,
... separator=" ").numpy()
array([b'abc 123', b'def 456'], dtype=object)
Args:
inputs: A `tf.string` tensor.
axis: Which axis to join along. The default behavior is to join all
elements, producing a scalar.
keepdims: If true, retains reduced dimensions with length 1.
separator: a string added between each string being joined.
name: A name for the operation (optional).
Returns:
A `tf.string` tensor.
"""
with ops.name_scope(None, "ReduceJoin", [inputs, axis]):
inputs_t = ops.convert_to_tensor(inputs)
axis = _reduce_join_reduction_dims(inputs_t, axis)
return gen_string_ops.reduce_join(
inputs=inputs_t,
reduction_indices=axis,
keep_dims=keepdims,
separator=separator,
name=name)
reduce_join.__doc__ = reduce_join_v2.__doc__
# This wrapper provides backwards compatibility for code that predates the
# unit argument and that passed 'name' as a positional argument.
@tf_export(v1=["strings.length"])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def string_length(input, name=None, unit="BYTE"):
"""Computes the length of each string given in the input tensor.
>>> strings = tf.constant(['Hello','TensorFlow', '🙂'])
>>> tf.strings.length(strings).numpy() # default counts bytes
array([ 5, 10, 4], dtype=int32)
>>> tf.strings.length(strings, unit="UTF8_CHAR").numpy()
array([ 5, 10, 1], dtype=int32)
Args:
input: A `Tensor` of type `string`. The strings for which to compute the
length for each element.
name: A name for the operation (optional).
unit: An optional `string` from: `"BYTE", "UTF8_CHAR"`. Defaults to
`"BYTE"`. The unit that is counted to compute string length. One of:
`"BYTE"` (for the number of bytes in each string) or `"UTF8_CHAR"` (for
the number of UTF-8 encoded Unicode code points in each string). Results
are undefined if `unit=UTF8_CHAR` and the `input` strings do not contain
structurally valid UTF-8.
Returns:
A `Tensor` of type `int32`, containing the length of the input string in
the same element of the input tensor.
"""
return gen_string_ops.string_length(input, unit=unit, name=name)
@tf_export("strings.length", v1=[])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def string_length_v2(input, unit="BYTE", name=None):
return gen_string_ops.string_length(input, unit=unit, name=name)
string_length_v2.__doc__ = gen_string_ops.string_length.__doc__
@tf_export(v1=["substr"])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
@deprecation.deprecated(None, "Use `tf.strings.substr` instead of `tf.substr`.")
def substr_deprecated(input, pos, len, name=None, unit="BYTE"):
return substr(input, pos, len, name=name, unit=unit)
substr_deprecated.__doc__ = gen_string_ops.substr.__doc__
@tf_export(v1=["strings.substr"])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def substr(input, pos, len, name=None, unit="BYTE"):
return gen_string_ops.substr(input, pos, len, unit=unit, name=name)
substr.__doc__ = gen_string_ops.substr.__doc__
@tf_export("strings.substr", v1=[])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def substr_v2(input, pos, len, unit="BYTE", name=None):
return gen_string_ops.substr(input, pos, len, unit=unit, name=name)
substr_v2.__doc__ = gen_string_ops.substr.__doc__
ops.NotDifferentiable("RegexReplace")
ops.NotDifferentiable("StringToHashBucket")
ops.NotDifferentiable("StringToHashBucketFast")
ops.NotDifferentiable("StringToHashBucketStrong")
ops.NotDifferentiable("ReduceJoin")
ops.NotDifferentiable("StringJoin")
ops.NotDifferentiable("StringSplit")
ops.NotDifferentiable("AsString")
ops.NotDifferentiable("EncodeBase64")
ops.NotDifferentiable("DecodeBase64")
@tf_export("strings.to_number", v1=[])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def string_to_number(input, out_type=dtypes.float32, name=None):
r"""Converts each string in the input Tensor to the specified numeric type.
(Note that int32 overflow results in an error while float overflow
results in a rounded value.)
Examples:
>>> tf.strings.to_number("1.55")
<tf.Tensor: shape=(), dtype=float32, numpy=1.55>
>>> tf.strings.to_number("3", tf.int32)
<tf.Tensor: shape=(), dtype=int32, numpy=3>
Args:
input: A `Tensor` of type `string`.
out_type: An optional `tf.DType` from: `tf.float32, tf.float64, tf.int32,
tf.int64`. Defaults to `tf.float32`.
The numeric type to interpret each string in `string_tensor` as.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
return gen_parsing_ops.string_to_number(input, out_type, name)
@tf_export(v1=["strings.to_number", "string_to_number"])
@dispatch.add_dispatch_support
def string_to_number_v1(
string_tensor=None,
out_type=dtypes.float32,
name=None,
input=None):
string_tensor = deprecation.deprecated_argument_lookup(
"input", input, "string_tensor", string_tensor)
return gen_parsing_ops.string_to_number(string_tensor, out_type, name)
string_to_number_v1.__doc__ = gen_parsing_ops.string_to_number.__doc__
@tf_export("strings.to_hash_bucket", v1=[])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def string_to_hash_bucket(input, num_buckets, name=None):
# pylint: disable=line-too-long
r"""Converts each string in the input Tensor to its hash mod by a number of buckets.
The hash function is deterministic on the content of the string within the
process.
Note that the hash function may change from time to time.
This functionality will be deprecated and it's recommended to use
`tf.strings.to_hash_bucket_fast()` or `tf.strings.to_hash_bucket_strong()`.
Examples:
>>> tf.strings.to_hash_bucket(["Hello", "TensorFlow", "2.x"], 3)
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([2, 0, 1])>
Args:
input: A `Tensor` of type `string`.
num_buckets: An `int` that is `>= 1`. The number of buckets.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int64`.
"""
# pylint: enable=line-too-long
return gen_string_ops.string_to_hash_bucket(input, num_buckets, name)
@tf_export(v1=["strings.to_hash_bucket", "string_to_hash_bucket"])
@dispatch.register_unary_elementwise_api
@dispatch.add_dispatch_support
def string_to_hash_bucket_v1( # pylint: disable=missing-function-docstring
string_tensor=None,
num_buckets=None,
name=None,
input=None):
string_tensor = deprecation.deprecated_argument_lookup(
"input", input, "string_tensor", string_tensor)
return gen_string_ops.string_to_hash_bucket(string_tensor, num_buckets, name)
string_to_hash_bucket_v1.__doc__ = gen_string_ops.string_to_hash_bucket.__doc__
@tf_export("strings.join", v1=["strings.join", "string_join"])
@dispatch.add_dispatch_support
@deprecation.deprecated_endpoints("string_join")
def string_join(inputs, separator="", name=None):
"""Perform element-wise concatenation of a list of string tensors.
Given a list of string tensors of same shape, performs element-wise
concatenation of the strings of the same index in all tensors.
>>> tf.strings.join(['abc','def']).numpy()
b'abcdef'
>>> tf.strings.join([['abc','123'],
... ['def','456'],
... ['ghi','789']]).numpy()
array([b'abcdefghi', b'123456789'], dtype=object)
>>> tf.strings.join([['abc','123'],
... ['def','456']],
... separator=" ").numpy()
array([b'abc def', b'123 456'], dtype=object)
The reduction version of this elementwise operation is
`tf.strings.reduce_join`
Args:
inputs: A list of `tf.Tensor` objects of same size and `tf.string` dtype.
separator: A string added between each string being joined.
name: A name for the operation (optional).
Returns:
A `tf.string` tensor.
"""
return gen_string_ops.string_join(inputs, separator=separator, name=name)
@tf_export("strings.unsorted_segment_join")
@dispatch.add_dispatch_support
def unsorted_segment_join(inputs,
segment_ids,
num_segments,
separator="",
name=None):
"""Joins the elements of `inputs` based on `segment_ids`.
Computes the string join along segments of a tensor.
Given `segment_ids` with rank `N` and `data` with rank `N+M`:
```
output[i, k1...kM] = strings.join([data[j1...jN, k1...kM])
```
where the join is over all `[j1...jN]` such that `segment_ids[j1...jN] = i`.
Strings are joined in row-major order.
For example:
>>> inputs = ['this', 'a', 'test', 'is']
>>> segment_ids = [0, 1, 1, 0]
>>> num_segments = 2
>>> separator = ' '
>>> tf.strings.unsorted_segment_join(inputs, segment_ids, num_segments,
... separator).numpy()
array([b'this is', b'a test'], dtype=object)
>>> inputs = [['Y', 'q', 'c'], ['Y', '6', '6'], ['p', 'G', 'a']]
>>> segment_ids = [1, 0, 1]
>>> num_segments = 2
>>> tf.strings.unsorted_segment_join(inputs, segment_ids, num_segments,
... separator=':').numpy()
array([[b'Y', b'6', b'6'],
[b'Y:p', b'q:G', b'c:a']], dtype=object)
Args:
inputs: A list of `tf.Tensor` objects of type `tf.string`.
segment_ids: A tensor whose shape is a prefix of `inputs.shape` and whose
type must be `tf.int32` or `tf.int64`. Negative segment ids are not
supported.
num_segments: A scalar of type `tf.int32` or `tf.int64`. Must be
non-negative and larger than any segment id.
separator: The separator to use when joining. Defaults to `""`.
name: A name for the operation (optional).
Returns:
A `tf.string` tensor representing the concatenated values, using the given
separator.
"""
return gen_string_ops.unsorted_segment_join(
inputs, segment_ids, num_segments, separator=separator, name=name)
# Register elementwise ops that don't have Python wrappers.
dispatch.register_unary_elementwise_api(gen_string_ops.as_string)
dispatch.register_unary_elementwise_api(gen_string_ops.decode_base64)
dispatch.register_unary_elementwise_api(gen_string_ops.encode_base64)
dispatch.register_unary_elementwise_api(gen_string_ops.string_lower)
dispatch.register_unary_elementwise_api(gen_string_ops.string_upper)
dispatch.register_unary_elementwise_api(gen_string_ops.unicode_transcode)
dispatch.register_unary_elementwise_api(gen_string_ops.string_strip)
dispatch.register_unary_elementwise_api(
gen_string_ops.string_to_hash_bucket_fast)
dispatch.register_unary_elementwise_api(
gen_string_ops.string_to_hash_bucket_strong)
dispatch.register_unary_elementwise_api(gen_string_ops.unicode_script)