|
- /**
- * 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 parsing_ops.h
- * \brief
- */
- #ifndef OPS_BUILT_IN_OP_PROTO_INC_PARSING_OPS_H_
- #define OPS_BUILT_IN_OP_PROTO_INC_PARSING_OPS_H_
-
- #include "graph/operator_reg.h"
- #include "graph/operator.h"
-
- namespace ge {
-
- /**
- *@brief Converts each string in the input Tensor to the specified numeric type . \n
-
- *@par Inputs:
- *Inputs include:
- *x: A Tensor. Must be one of the following types: string . \n
-
- *@par Attributes:
- *out_type: The numeric type to interpret each string in string_tensor as . \n
-
- *@par Outputs:
- *y: A Tensor. Has the same type as x . \n
-
- *@attention Constraints:
- *The implementation for StringToNumber on Ascend uses AICPU, with bad performance. \n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow StringToNumber operator.
- */
- REG_OP(StringToNumber)
- .INPUT(x, TensorType({DT_STRING}))
- .OUTPUT(y, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64}))
- .ATTR(out_type, Type, DT_FLOAT)
- .OP_END_FACTORY_REG(StringToNumber)
-
- /**
- *@brief Convert serialized tensorflow.TensorProto prototype to Tensor.
- *@brief Parse an Example prototype.
- *@par Inputs:
- *@li serialized: A Tensor of type string.
- *@li dense_defaults: DYNAMIC INPUT Tensor type as string, float, int64. \n
-
- *@par Attributes:
- *@li num_sparse: type int num of inputs sparse_indices , sparse_values, sparse_shapes
- *@li sparse_keys: ListString
- *@li sparse_types: types of sparse_values
- *@li dense_keys: ListString
- *@li Tdense: output of dense_defaults type
- *@li dense_shapes: output of dense_defaults shape \n
-
- *@par Outputs:
- *@li sparse_indices: A Tensor of type string.
- *@li sparse_values: Has the same type as sparse_types.
- *@li sparse_shapes: A Tensor of type int64
- *@li dense_values: Has the same type as dense_defaults.
-
- *Warning: THIS FUNCTION IS EXPERIMENTAL. Please do not use.
- */
- REG_OP(ParseSingleExample)
- .INPUT(serialized, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(dense_defaults, TensorType({DT_STRING,DT_FLOAT,DT_INT64}))
- .DYNAMIC_OUTPUT(sparse_indices, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(sparse_values, TensorType({DT_STRING,DT_FLOAT,DT_INT64}))
- .DYNAMIC_OUTPUT(sparse_shapes, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(dense_values, TensorType({DT_STRING,DT_FLOAT,DT_INT64}))
- .ATTR(num_sparse, Int, 0)
- .ATTR(sparse_keys, ListString, {})
- .ATTR(dense_keys, ListString, {})
- .ATTR(sparse_types, ListType, {})
- .ATTR(Tdense, ListType, {})
- .ATTR(dense_shapes, ListListInt, {})
- .OP_END_FACTORY_REG(ParseSingleExample)
-
- /**
- *@brief Decodes raw file into tensor . \n
- *@par Inputs:
- *bytes: A Tensor of type string.
-
- *@par Attributes:
- *@li little_endian: bool ture
- *@li out_type: output type
-
- *@par Outputs:
- *Output: A Tensor
- */
- REG_OP(DecodeRaw)
- .INPUT(bytes, TensorType({DT_STRING}))
- .OUTPUT(output, TensorType({DT_BOOL,DT_FLOAT16,DT_DOUBLE,DT_FLOAT,
- DT_INT64,DT_INT32,DT_INT8,DT_UINT8,DT_INT16,
- DT_UINT16,DT_COMPLEX64,DT_COMPLEX128}))
- .ATTR(out_type, Type, DT_FLOAT)
- .ATTR(little_endian, Bool, true)
- .OP_END_FACTORY_REG(DecodeRaw)
-
- /**
- *@brief Convert serialized tensorflow.TensorProto prototype to Tensor. \n
-
- *@par Inputs:
- *serialized: A Tensor of string type. Scalar string containing serialized
- *TensorProto prototype. \n
-
- *@par Attributes:
- *out_type: The type of the serialized tensor. The provided type must match the
- *type of the serialized tensor and no implicit conversion will take place. \n
-
- *@par Outputs:
- *output: A Tensor of type out_type. \n
-
- *@attention Constraints:
- *The implementation for StringToNumber on Ascend uses AICPU,
- *with badperformance. \n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow ParseTensor operator.
- */
- REG_OP(ParseTensor)
- .INPUT(serialized, TensorType({DT_STRING}))
- .OUTPUT(output, TensorType(DT_FLOAT, DT_FLOAT16, DT_INT8, DT_INT16,
- DT_UINT16, DT_UINT8, DT_INT32, DT_INT64, DT_UINT32,
- DT_UINT64, DT_BOOL, DT_DOUBLE, DT_STRING,
- DT_COMPLEX64, DT_COMPLEX128}))
- .ATTR(out_type, Type, DT_FLOAT)
- .OP_END_FACTORY_REG(ParseTensor)
-
- /**
- *@brief Converts each string in the input Tensor to the specified numeric
- *type . \n
-
- *@par Inputs:
- *Inputs include:
- *@li records: Each string is a record/row in the csv and all records should have the
- *same format. \n
- *@li record_defaults: One tensor per column of the input record, with either a
- *scalar default value for that column or an empty vector if the column is
- *required. \n
-
- *@par Attributes:
- *@li OUT_TYPE: The numeric type to interpret each string in string_tensor as . \n
- *@li field_delim: char delimiter to separate fields in a record. \n
- *@li use_quote_delim: If false, treats double quotation marks as regular characters
- *inside of the string fields (ignoring RFC 4180, Section 2, Bullet 5). \n
- *@li na_value: Additional string to recognize as NA/NaN. \n
- *@li select_cols: Optional sorted list of column indices to select. If specified,
- only this subset of columns will be parsed and returned.
-
- *@par Outputs:
- *output: A Tensor. Has the same type as x . \n
-
- *@attention Constraints:
- *The implementation for StringToNumber on Ascend uses AICPU, with bad
- *performance. \n
-
- *@par Third-party framework compatibility
- *@li compatible with tensorflow StringToNumber operator.
- */
- REG_OP(DecodeCSV)
- .INPUT(records, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(record_defaults, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32,
- DT_INT64, DT_STRING}))
- .DYNAMIC_OUTPUT(output, TensorType({DT_FLOAT, DT_DOUBLE, DT_INT32,
- DT_INT64, DT_STRING}))
- .ATTR(OUT_TYPE, ListType, {})
- .ATTR(field_delim, String, ",")
- .ATTR(use_quote_delim, Bool, true)
- .ATTR(na_value, String, ",")
- .ATTR(select_cols, ListInt, {})
- .OP_END_FACTORY_REG(DecodeCSV)
-
- /**
- *@brief Convert serialized tensorflow.TensorProto prototype to Tensor.
- *@brief Parse an Example prototype.
- *@par Inputs:
- *@li serialized: A Tensor of type string. \n
- *@li name:A Tensor of type string. \n
- *@li sparse_keys: Dynamic input tensor of string. \n
- *@li dense_keys: Dynamic input tensor of string \n
- *@li dense_defaults: Dynamic input tensor type as string, float, int64. \n
-
- *@par Attributes:
- *@li Nsparse: Number of sparse_keys, sparse_indices and sparse_shapes \n
- *@li Ndense: Number of dense_keys \n
- *@li sparse_types: types of sparse_values \n
- *@li Tdense: Type of dense_defaults dense_defaults and dense_values \n
- *@li dense_shapes: output of dense_defaults shape \n
-
- *@par Outputs:
- *@li sparse_indices: A Tensor of type string. \n
- *@li sparse_values: Has the same type as sparse_types. \n
- *@li sparse_shapes: A Tensor of type int64 \n
- *@li dense_values: Has the same type as dense_defaults. \n
- *@par Third-party framework compatibility \n
- *@li compatible with tensorflow StringToNumber operator. \n
- */
- REG_OP(ParseExample)
- .INPUT(serialized, TensorType({DT_STRING}))
- .INPUT(name, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(sparse_keys, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(dense_keys, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(dense_defaults, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .DYNAMIC_OUTPUT(sparse_indices, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(sparse_values, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .DYNAMIC_OUTPUT(sparse_shapes, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(dense_values, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .ATTR(Nsparse, Int, 0)
- .ATTR(Ndense, Int, 0)
- .ATTR(sparse_types, ListType, {})
- .ATTR(Tdense, ListType, {})
- .ATTR(dense_shapes, ListListInt, {})
- .OP_END_FACTORY_REG(ParseExample)
-
- /**
- *@brief Transforms a scalar brain.SequenceExample proto (as strings) into typed
- *tensors.
- *@par Inputs:
- *@li serialized: A Tensor of type string. \n
- *@li feature_list_dense_missing_assumed_empty:A Tensor of type string. \n
- *@li context_sparse_keys: Dynamic input tensor of string. \n
- *@li context_dense_keys: Dynamic input tensor of string \n
- *@li feature_list_sparse_keys: Dynamic input tensor of string \n
- *@li feature_list_dense_keys: Dynamic input tensor of string \n
- *@li context_dense_defaults: Dynamic input tensor of string, float, int64 \n
- *@li debug_name: A Tensor of type string. \n
-
- *@par Attributes:
- *@li Ncontext_sparse: Number of context_sparse_keys, context_sparse_indices and context_sparse_shapes \n
- *@li Ncontext_dense: Number of context_dense_keys \n
- *@li Nfeature_list_sparse: Number of feature_list_sparse_keys \n
- *@li Nfeature_list_dense: Number of feature_list_dense_keys \n
- *@li context_sparse_types: Types of context_sparse_values \n
- *@li Tcontext_dense: Number of dense_keys \n
- *@li feature_list_dense_types: Types of feature_list_dense_values \n
- *@li context_dense_shapes: Shape of context_dense \n
- *@li feature_list_sparse_types: Type of feature_list_sparse_values \n
- *@li feature_list_dense_shapes: Shape of feature_list_dense \n
-
- *@par Outputs:
- *@li context_sparse_indices: Dynamic output tensor of type int64. \n
- *@li context_sparse_values: Dynamic output tensor of type string, float, int64. \n
- *@li context_sparse_shapes: Dynamic output tensor of type int64 \n
- *@li context_dense_values: Dynamic output tensor of type string, float, int64. \n
- *@li feature_list_sparse_indices: Dynamic output tensor of type int64. \n
- *@li feature_list_sparse_values: Dynamic output tensor of type string, float, int64. \n
- *@li feature_list_sparse_shapes: Dynamic output tensor of type int64 \n
- *@li feature_list_dense_values: Dynamic output tensor of type string, float, int64. \n
- *@par Third-party framework compatibility \n
- *@li compatible with tensorflow StringToNumber operator. \n
- */
- REG_OP(ParseSingleSequenceExample)
- .INPUT(serialized, TensorType({DT_STRING}))
- .INPUT(feature_list_dense_missing_assumed_empty, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(context_sparse_keys, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(context_dense_keys, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(feature_list_sparse_keys, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(feature_list_dense_keys, TensorType({DT_STRING}))
- .DYNAMIC_INPUT(context_dense_defaults, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .INPUT(debug_name, TensorType({DT_STRING}))
- .DYNAMIC_OUTPUT(context_sparse_indices, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(context_sparse_values, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .DYNAMIC_OUTPUT(context_sparse_shapes, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(context_dense_values, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .DYNAMIC_OUTPUT(feature_list_sparse_indices, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(feature_list_sparse_values, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .DYNAMIC_OUTPUT(feature_list_sparse_shapes, TensorType({DT_INT64}))
- .DYNAMIC_OUTPUT(feature_list_dense_values, TensorType({DT_FLOAT, DT_INT64, DT_STRING}))
- .ATTR(Ncontext_sparse, Int, 0)
- .ATTR(Ncontext_dense, Int, 0)
- .ATTR(Nfeature_list_sparse, Int, 0)
- .ATTR(Nfeature_list_dense, Int, 0)
- .ATTR(context_sparse_types, ListType, {})
- .ATTR(Tcontext_dense, ListType, {})
- .ATTR(feature_list_dense_types, ListType, {})
- .ATTR(context_dense_shapes, ListListInt, {})
- .ATTR(feature_list_sparse_types, ListType, {})
- .ATTR(feature_list_dense_shapes, ListListInt, {})
- .OP_END_FACTORY_REG(ParseSingleSequenceExample)
-
- } // namespace ge
-
- #endif // OPS_BUILT_IN_OP_PROTO_INC_PARSING_OPS_H_
|