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- /**
- * Copyright 2019 Huawei Technologies Co., Ltd
- *
- * 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.
- */
-
- /*!
- * \file string_ops.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_STRING_OPS_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_STRING_OPS_H_
-
- #include <sstream>
- #include "graph/operator_reg.h"
-
- namespace ge {
-
- /**
- *@brief Split elements of input based on delimiter into a SparseTensor . \n
-
- *@par Inputs:
- include:
- *@li input:1-D. Strings to split.
- *@li delimiter:0-D. Delimiter characters (bytes), or empty string . \n
-
- *@par Attributes:
- * skip_empty:A bool. If True, skip the empty strings from the result . \n
-
- *@par Outputs:
- *@li indices:A dense matrix of int64 representing the indices of the sparse tensor.
- *@li values:A vector of strings corresponding to the splited values.
- *@li shape:A length-2 vector of int64 representing the shape of the sparse tensor,
- *where the first value is N and the second value is the maximum number of tokens
- *in a single input entry . \n
-
- *@see StringSplit()
-
- *@par Third-party framework compatibility
- *compatible with StringSplit op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringSplit)
- .INPUT(input, TensorType({DT_STRING}))
- .INPUT(delimiter, TensorType({DT_STRING}))
- .OUTPUT(indices, TensorType({DT_INT64}))
- .OUTPUT(values, TensorType({DT_STRING}))
- .OUTPUT(shape, TensorType({DT_INT64}))
- .ATTR(skip_empty, Bool, true)
- .OP_END_FACTORY_REG(StringSplit)
-
- /**
- *@brief Split elements of source based on sep into a SparseTensor . \n
-
- *@par Inputs:
- include:
- *@li input:1-D. Strings to split.
- *@li sep:0-D string Tensor, the delimiter character . \n
-
- *@par Attributes:
- * maxsplit:An int. If maxsplit > 0, limit of the split of the result . \n
-
- *@par Outputs:
- *@li indices:A dense matrix of int64 representing the indices of the sparse tensor.
- *@li values:A vector of strings corresponding to the splited values.
- *@li shape:A length-2 vector of int64 representing the shape of the sparse tensor,
- *where the first value is N and the second value is the maximum number of tokens
- *in a single input entry . \n
-
- *@see StringSplitV2()
-
- *@par Third-party framework compatibility
- *compatible with StringSplitV2 op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringSplitV2)
- .INPUT(input, TensorType({DT_STRING}))
- .INPUT(sep, TensorType({DT_STRING}))
- .OUTPUT(indices, TensorType({DT_INT64}))
- .OUTPUT(values, TensorType({DT_STRING}))
- .OUTPUT(shape, TensorType({DT_INT64}))
- .ATTR(maxsplit, Int, -1)
- .OP_END_FACTORY_REG(StringSplitV2)
-
- /**
- *@brief Determine the script codes of a given tensor of Unicode integer code points . \n
-
- *@par Inputs:
- include:
- *x:A Tensor of int32 Unicode code points . \n
-
- *@par Outputs:
- *y:A Tensor of int32 script codes corresponding to each input code point . \n
-
- *@attention Constraints:
- *This operation converts Unicode code points to script codes corresponding to
- *each code point. Script codes correspond to International Components for
- *Unicode (ICU) UScriptCode values.
- *See http://icu-project.org/apiref/icu4c/uscript_8h.html.
- *Returns -1 (USCRIPT_INVALID_CODE) for invalid codepoints.
- *Output shape will match input shape . \n
-
- *@see UnicodeScript()
-
- *@par Third-party framework compatibility
- *compatible with UnicodeScript op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(UnicodeScript)
- .INPUT(x, TensorType({DT_INT32}))
- .OUTPUT(y, TensorType({DT_INT32}))
- .OP_END_FACTORY_REG(UnicodeScript)
-
- /**
- *@brief Return substrings from Tensor of strings . \n
-
- *@par Inputs:
- include:
- *@li input:Tensor of strings.
- *@li pos:Scalar defining the position of first character in each substring.
- *@li len:Scalar defining the number of characters to include in each substring . \n
-
- *@par Outputs:
- *output:Tensor of substrings . \n
-
- *@attention Constraints:
- *The hash function is deterministic on the content of the string within
- *the process and will never change. However, it is not suitable for
- *cryptography. This function may be used when CPU time is scarce and
- *inputs are trusted or unimportant. There is a risk of adversaries
- *constructing inputs that all hash to the same bucket.
- *To prevent this problem, use a strong hash function with
- *tf.string_to_hash_bucket_strong . \n
-
- *@see Substr()
-
- *@par Third-party framework compatibility
- *compatible with Substr op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(Substr)
- .INPUT(input, TensorType({DT_STRING}))
- .INPUT(pos, TensorType({DT_INT32, DT_INT64}))
- .INPUT(len, TensorType({DT_INT32, DT_INT64}))
- .OUTPUT(output, TensorType({DT_STRING}))
- .OP_END_FACTORY_REG(Substr)
-
- /**
- *@brief Converts each string in the input Tensor to its hash mod by a number of buckets . \n
-
- *@par Inputs:
- include:
- *string_tensor:The strings to assign a hash bucket . \n
-
- *@par Outputs:
- *y:A Tensor of the same shape as the input x . \n
-
- *@attention Constraints:
- *The hash function is deterministic on the content of the string within
- *the process and will never change. However, it is not suitable for cryptography.
- *This function may be used when CPU time is scarce and inputs are trusted or
- *unimportant. There is a risk of adversaries constructing inputs that all hash
- *to the same bucket. To prevent this problem, use a strong hash function with
- *tf.string_to_hash_bucket_strong . \n
-
- *@see StringToHashBucketFast()
-
- *@par Third-party framework compatibility
- *compatible with StringToHashBucketFast op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringToHashBucketFast)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_INT64}))
- .ATTR(num_buckets, Int, 1)
- .OP_END_FACTORY_REG(StringToHashBucketFast)
-
- /**
- *@brief Converts each string in the input Tensor to its hash mod by a number of buckets . \n
-
- *@par Inputs:
- include:
- *x:The strings to assign a hash bucket . \n
-
- *@par Attributes:
- *num_buckets:The number of buckets . \n
-
- *@par Outputs:
- *y:A Tensor of the same shape as the input x . \n
-
- *@attention Constraints:
- *@li A strong hash is important when inputs may be malicious, e.g. URLs with
- *additional components. Adversaries could try to make their inputs hash to
- *the same bucket for a denial-of-service attack or to skew the results.
- *A strong hash can be used to make it difficult to find inputs with a skewed
- * hash value distribution over buckets. This requires that the hash function\
- *is seeded by a high-entropy (random) "key" unknown to the adversary.
- *@li The additional robustness comes at a cost of roughly 4x higher
- *compute time than tf.string_to_hash_bucket_fast . \n
-
- *@see StringToHashBucketStrong()
-
- *@par Third-party framework compatibility
- *compatible with StringToHashBucketStrong op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringToHashBucketStrong)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_INT64}))
- .ATTR(num_buckets, Int, 1)
- .REQUIRED_ATTR(key, ListInt)
- .OP_END_FACTORY_REG(StringToHashBucketStrong)
-
- /**
- *@brief Converts each string in the input Tensor to its hash mod by a number of buckets . \n
-
- *@par Inputs:
- include:
- *string_tensor:The strings to assign a hash bucket . \n
-
- *@par Attributes:
- *num_buckets:The number of buckets . \n
-
- *@par Outputs:
- *y:A Tensor of the same shape as the input string_tensor . \n
-
- *@see StringToHashBucket()
-
- *@par Third-party framework compatibility
- *compatible with StringToHashBucket op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringToHashBucket)
- .INPUT(string_tensor, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_INT64}))
- .ATTR(num_buckets, Int, 1)
- .OP_END_FACTORY_REG(StringToHashBucket)
-
- /**
- *@brief Strip leading and trailing whitespaces from the Tensor . \n
-
- *@par Inputs:
- include:
- *x:A string Tensor of any shape . \n
-
- *@par Outputs:
- *y:A string Tensor of the same shape as the input . \n
-
- *@see StringStrip()
-
- *@par Third-party framework compatibility
- *compatible with StringStrip op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringStrip)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .OP_END_FACTORY_REG(StringStrip)
-
- /**
- *@brief Computes the length of each string given in the input tensor . \n
-
- *@par Inputs:
- include:
- *x:The string for which to compute the length . \n
-
- *@par Attributes:
- *unit: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 . \n
-
- *@par Outputs:
- *y:Integer tensor that has the same shape as input.
- *The output contains the element-wise string lengths of input . \n
-
- *@see StringLength()
-
- *@par Third-party framework compatibility
- *compatible with StringLength op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringLength)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_INT32}))
- .ATTR(unit, String, "BYTE")
- .OP_END_FACTORY_REG(StringLength)
-
- /**
- *@brief Joins the strings in the given list of string tensors into one tensor . \n
-
- *@par Inputs:
- *The input is a string tensor of any shape. The pattern is a scalar string tensor
- *which is applied to every element of the input tensor. The boolean values
- *(True or False) of the output tensor indicate if the input matches the regex
- *pattern provided. The pattern follows the re2 syntax
- *(https://github.com/google/re2/wiki/Syntax).:
- include:
- *x:A list of string tensors. The tensors must all have the same shape,
- *or be scalars. Scalars may be mixed in; these will be broadcast to the shape
- *of non-scalar inputs . It's a dynamic input. \n
-
- *@par Attributes:
- *@li N:The length of input x.
- *@li separator:string, an optional join separator . \n
-
- *@par Outputs:
- *y:The output tensor . \n
-
- *@see StringJoin()
-
- *@par Third-party framework compatibility
- *compatible with StringJoin op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringJoin)
- .DYNAMIC_INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .REQUIRED_ATTR(N, Int)
- .ATTR(separator, String, "")
- .OP_END_FACTORY_REG(StringJoin)
-
- /**
- *@brief Formats a string template using a list of tensors . \n
-
- *@par Inputs:
- *The input is a string tensor of any shape. The pattern is a scalar string tensor
- *which is applied to every element of the input tensor.
- *The boolean values (True or False) of the output tensor indicate if the input
- *matches the regex pattern provided. The pattern follows the re2 syntax
- *(https://github.com/google/re2/wiki/Syntax).:
- include:
- *x:The tensors to format into the placeholder string . It's a dynamic input. \n
-
- *@par Attributes:
- *@li template:A string, the template to format tensor summaries into.
- *@li placeholder:A string, at each placeholder in the template a subsequent tensor summary will be inserted.
- *@li summarize:When formatting the tensor summaries print the first and last summarize entries of each tensor dimension . \n
-
- *@par Outputs:
- *y:The resulting string scalar . \n
-
- *@see StringFormat()
-
- *@par Third-party framework compatibility
- * compatible with StringFormat op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(StringFormat)
- .DYNAMIC_INPUT(x, TensorType({DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, \
- DT_INT32, DT_INT64, DT_UINT32, DT_UINT64, DT_STRING, DT_FLOAT16, \
- DT_FLOAT, DT_DOUBLE, DT_BOOL}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .ATTR(template, String, "%s")
- .ATTR(placeholder, String, "%s")
- .ATTR(summarize, Int, 3)
- .OP_END_FACTORY_REG(StringFormat)
-
- /**
- *@brief Check if the input matches the regex pattern . \n
-
- *@par Inputs:
- *The input is a string tensor of any shape. The pattern is a scalar string tensor
- *which is applied to every element of the input tensor. The boolean values
- *(True or False) of the output tensor indicate if the input matches the regex
- *pattern provided. The pattern follows the re2 syntax
- *(https://github.com/google/re2/wiki/Syntax).:
- include:
- *@li x:A string tensor of the text to be processed.
- *@li pattern:A scalar string tensor containing the regular expression to match the input . \n
-
- *@par Outputs:
- *y:A bool tensor with the same shape as input . \n
-
- *@see RegexFullMatch()
-
- *@par Third-party framework compatibility
- *compatible with RegexFullMatch op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(RegexFullMatch)
- .INPUT(x, TensorType({DT_STRING}))
- .INPUT(pattern, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_BOOL}))
- .OP_END_FACTORY_REG(RegexFullMatch)
-
- /**
- *@brief Replaces matches of the pattern regular expression in input with the
- *replacement string provided in rewrite . \n
-
- *@par Inputs:
- *It follows the re2 syntax (https://github.com/google/re2/wiki/Syntax).:
- include:
- *@li x:The text to be processed.
- *@li pattern:The regular expression to be matched in the input strings.
- *@li rewrite:The rewrite string to be substituted for the pattern expression
- *where it is matched in the input strings . \n
-
- *@par Attributes:
- *replace_global:If True, the replacement is global
- *(that is, all matches of the pattern regular expression in each input string
- *are rewritten), otherwise the rewrite substitution is only made for the first
- * pattern match . \n
-
- *@par Outputs:
- *y:The text after applying pattern match and rewrite substitution . \n
-
- *@see RegexReplace()
-
- *@par Third-party framework compatibility
- *compatible with RegexReplace op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(RegexReplace)
- .INPUT(x, TensorType({DT_STRING}))
- .INPUT(pattern, TensorType({DT_STRING}))
- .INPUT(rewrite, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .ATTR(replace_global, Bool, true)
- .OP_END_FACTORY_REG(RegexReplace)
-
- /**
- *@brief Converts each entry in the given tensor to strings . \n
-
- *@par Inputs:
- *Supports many numeric types and boolean.:
- include:
- *x:A tensor can be trans to string . \n
-
- *@par Attributes:
- *@li precision:The post-decimal precision to use for floating point numbers.
- *Only used if precision > -1.
- *@li scientific:Use scientific notation for floating point numbers.
- *@li shortest:Use shortest representation (either scientific or standard)
- *for floating point numbers..
- *@li width:Pad pre-decimal numbers to this width. Applies to both floating
- *point and integer numbers. Only used if width > -1.
- *@li fill:The value to pad if width > -1. If empty, pads with spaces.
- *Another typical value is '0'. String cannot be longer than 1 character . \n
-
- *@par Outputs:
- *y:The output tensor . \n
-
- *@see AsString()
-
- *@par Third-party framework compatibility
- *compatible with AsString op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(AsString)
- .INPUT(x, TensorType({DT_INT8, DT_INT16, DT_INT32, DT_INT64, DT_FLOAT, \
- DT_DOUBLE, DT_BOOL}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .ATTR(precision, Int, -1)
- .ATTR(scientific, Bool, false)
- .ATTR(shortest, Bool, false)
- .ATTR(width, Int, -1)
- .ATTR(fill, String, "")
- .OP_END_FACTORY_REG(AsString)
-
- /**
- *@brief Encode strings into web-safe base64 format . \n
-
- *@par Inputs:
- *Input may or may not have padding at the end. See EncodeBase64 for padding.
- *Web-safe means that input must use - and _ instead of + and /.:
- include:
- *x:Strings to be encoded . \n
-
- *@par Attributes:
- *pad:Bool whether padding is applied at the ends . \n
-
- *@par Outputs:
- *y:Input strings encoded in base64 . \n
-
- *@attention Constraints:
- *Refer to the following article for more information on base64 format:
- *en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '='
- *at the end so that the encoded has length multiple of 4.
- *See Padding section of the link above. Web-safe means that the encoder
- *uses - and _ instead of + and / . \n
-
- *@see EncodeBase64()
-
- *@par Third-party framework compatibility
- *compatible with EncodeBase64 op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(EncodeBase64)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .ATTR(pad, Bool, false)
- .OP_END_FACTORY_REG(EncodeBase64)
-
- /**
- *@brief Decode web-safe base64-encoded strings . \n
-
- *@par Inputs:
- *Input may or may not have padding at the end. See EncodeBase64 for padding.
- *Web-safe means that input must use - and _ instead of + and /.:
- include:
- *x:Base64 strings to decode . \n
-
- *@par Outputs:
- *y:Decoded strings . \n
-
- *@see DecodeBase64()
-
- *@par Third-party framework compatibility
- *compatible with DecodeBase64 op of tensorflow
-
- *@par Restrictions:
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(DecodeBase64)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_STRING}))
- .OP_END_FACTORY_REG(DecodeBase64)
- } // namespace ge
-
- #endif // OPS_BUILT_IN_OP_PROTO_INC_STRING_OPS_H_
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