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98 lines
4.1 KiB
98 lines
4.1 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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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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/*!
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* \file ragged_conversion_ops.h
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* \brief
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*/
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#ifndef OPS_BUILT_IN_OP_PROTO_INC_RAGGED_CONVERSION_OPS_H_
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#define OPS_BUILT_IN_OP_PROTO_INC_RAGGED_CONVERSION_OPS_H_
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Converts a RaggedTensor into a SparseTensor with the same values . \n
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*@par Inputs:
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*Two inputs, including:
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*@li rt_nested_splits: A list of at least 1 Tensor objects with the same type
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in: int32, int64. The row_splits for the RaggedTensor. It's a dynamic input.
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*@li rt_dense_values: A Tensor. The flat_values for the RaggedTensor
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Must be one of the following types: bool, int8, int16, uint16, int32,
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int64, double, float, float16 . \n
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*@par Attributes:
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*@li RAGGED_RANK: the dynamic of input rt_nested_splits with type int.
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*@li Tsplits: A required attribute, the type is int64 . \n
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*@par Outputs:
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*@li sparse_indices: A Tensor of type int64.
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*@li sparse_values: A Tensor. Has the same type as rt_dense_values.
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*@li sparse_dense_shape: A Tensor of type int64 . \n
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*@par Third-party framework compatibility
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* Compatible with TensorFlow operator RaggedTensorToSparse.
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*/
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REG_OP(RaggedTensorToSparse)
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.DYNAMIC_INPUT(rt_nested_splits, TensorType({DT_INT32, DT_INT64}))
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.INPUT(rt_dense_values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
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.OUTPUT(sparse_indices, TensorType({DT_INT64}))
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.OUTPUT(sparse_values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
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.OUTPUT(sparse_dense_shape, TensorType({DT_INT64}))
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.ATTR(RAGGED_RANK, Int, 1)
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.ATTR(Tsplits, Type, DT_INT64)
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.OP_END_FACTORY_REG(RaggedTensorToSparse)
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/**
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*@brief Create a dense tensor from a ragged tensor, possibly altering its shape . \n
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*@par Inputs:
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*Six inputs, including:
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*@li shape:A `Tensor`. Must be one of the following types: `int64`, `int32`.
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*@li values:A 1D tensor representing the values of the ragged tensor.
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*@li default_value:A `Tensor`. Must have the same type as `values`.
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*@li row_partition_tensors:A list of at least 1 `Tensor` objects with the same
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type in: `int64`, `int32` . It's a dynamic input.\n
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*@par Attributes:
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*@li num_row_partition_tensors:Numbers of row partition tensors.
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*@li row_partition_types: A list of `strings`.
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The types of the row partition tensors. At present, these can be:
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* "ROW_SPLITS": the row_splits tensor from the ragged tensor.
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* "VALUE_ROWIDS": the value_rowids tensor from the ragged tensor.
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* "FIRST_DIM_SIZE": if value_rowids is used for the first dimension, then it
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is preceeded by "FIRST_DIM_SIZE" . \n
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*@par Outputs:
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*@li result: A `Tensor`. Has the same type as `values`.
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*/
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REG_OP(RaggedTensorToTensor)
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.INPUT(shape, TensorType({DT_INT32, DT_INT64}))
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.INPUT(values, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
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DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
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.INPUT(default_value, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16,
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DT_UINT16, DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
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.DYNAMIC_INPUT(row_partition_tensors, TensorType({DT_INT32, DT_INT64}))
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.OUTPUT(result, TensorType({DT_BOOL, DT_INT8, DT_UINT8, DT_INT16, DT_UINT16,
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DT_INT32, DT_INT64, DT_DOUBLE, DT_FLOAT, DT_FLOAT16}))
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.REQUIRED_ATTR(num_row_partition_tensors, Int)
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.REQUIRED_ATTR(row_partition_types, ListString)
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.OP_END_FACTORY_REG(RaggedTensorToTensor)
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} // namespace ge
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#endif // OPS_BUILT_IN_OP_PROTO_INC_RAGGED_CONVERSION_OPS_H_
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