forked from huawei/mindspore2022
71 lines
2.7 KiB
C++
71 lines
2.7 KiB
C++
/**
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* Copyright 2019 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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#include "dataset/kernels/tensor_op.h"
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#include <iostream>
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#include <memory>
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#include <mutex>
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#include <vector>
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namespace mindspore {
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namespace dataset {
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// Name: Compute()
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// Description: This Compute() take 1 Tensor and produce 1 Tensor.
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// The derived class should override this function otherwise error.
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Status TensorOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
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IO_CHECK(input, output);
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if (!OneToOne()) {
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return Status(StatusCode::kUnexpectedError, "Wrong Compute() function is called. This is not 1-1 TensorOp.");
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} else {
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return Status(StatusCode::kUnexpectedError,
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"Is this TensorOp 1-1? If yes, please implement this Compute() in the derived class.");
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}
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}
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// Name: Compute()
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// Description: This Compute() take multiple Tensors from different columns and produce multiple Tensors too.
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// The derived class should override this function otherwise error.
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Status TensorOp::Compute(const std::vector<std::shared_ptr<Tensor>> &input,
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std::vector<std::shared_ptr<Tensor>> *output) {
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IO_CHECK_VECTOR(input, output);
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if (OneToOne()) {
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output->resize(1);
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return Compute(input[0], &(*output)[0]);
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}
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return Status(StatusCode::kUnexpectedError,
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"Is this TensorOp oneToOne? If no, please implement this Compute() in the derived class.");
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}
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void TensorOp::Print(std::ostream &out) const { out << "TensorOp" << std::endl; }
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Status TensorOp::OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) {
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if (inputs.size() != NumInput())
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return Status(StatusCode::kUnexpectedError,
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"The size of the input argument vector does not match the number of inputs");
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outputs = inputs;
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return Status::OK();
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}
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Status TensorOp::OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) {
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if (inputs.size() != NumInput())
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return Status(StatusCode::kUnexpectedError,
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"The size of the input argument vector does not match the number of inputs");
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outputs = inputs;
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return Status::OK();
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}
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} // namespace dataset
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} // namespace mindspore
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