forked from huawei/mindspore2022
90 lines
3.1 KiB
C++
90 lines
3.1 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/engine/data_buffer.h"
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#include "dataset/util/allocator.h"
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#include "dataset/core/global_context.h"
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#include "dataset/core/tensor.h"
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namespace mindspore {
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namespace dataset {
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// Name: Constructor #1
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// Description: This is the main constructor that is used for making a buffer
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DataBuffer::DataBuffer(int32_t id, BufferFlags flags) : buffer_id_(id), tensor_table_(nullptr), buffer_flags_(flags) {}
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// A method for debug printing of the buffer
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void DataBuffer::Print(std::ostream &out, bool show_all) const {
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out << "bufferId: " << buffer_id_ << "\nflags: " << std::hex << buffer_flags_ << std::dec << "\n";
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// If the column counts are set then it means that data has been set into
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// the tensor table. Display the tensor table here.
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if (this->NumCols() > 0) {
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out << "Tensor table:\n";
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for (int32_t row = 0; row < DataBuffer::NumRows(); ++row) {
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out << "Row # : " << row << "\n";
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TensorRow currRow = (*tensor_table_)[row];
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for (int32_t col = 0; col < this->NumCols(); ++col) {
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out << "Column #: " << col << "\n"; // Should add the column name here as well?
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// Call the tensor display
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out << *(currRow[col]) << "\n";
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}
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}
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}
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}
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// Remove me!! Callers should fetch rows via pop
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Status DataBuffer::GetTensor(std::shared_ptr<Tensor> *ptr, int32_t row_id, int32_t col_id) const {
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if (row_id < tensor_table_->size() && col_id < tensor_table_->at(row_id).size()) {
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*ptr = (tensor_table_->at(row_id)).at(col_id);
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} else {
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std::string err_msg =
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"indices for mTensorTable out of range: (" + std::to_string(row_id) + "," + std::to_string(col_id) + ").";
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RETURN_STATUS_UNEXPECTED(err_msg);
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}
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return Status::OK();
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}
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// Remove me!! Callers should fetch rows via pop
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Status DataBuffer::GetRow(int32_t row_id, TensorRow *ptr) const {
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if (tensor_table_ && !tensor_table_->empty() && row_id < tensor_table_->size()) {
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*ptr = tensor_table_->at(row_id);
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} else {
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std::string err_msg = "rowId for mTensorTable out of range: " + std::to_string(row_id);
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RETURN_STATUS_UNEXPECTED(err_msg);
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}
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return Status::OK();
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}
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Status DataBuffer::PopRow(TensorRow *ptr) {
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if (tensor_table_ && !tensor_table_->empty()) {
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*ptr = std::move(tensor_table_->front());
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tensor_table_->pop_front();
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}
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return Status::OK();
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}
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Status DataBuffer::SliceOff(int64_t number_of_rows) {
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while (number_of_rows > 0) {
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tensor_table_->pop_back();
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number_of_rows--;
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}
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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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