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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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.
*/
#include "ir/Operand.h"
namespace neurun
{
namespace ir
{
size_t Operand::operandSize(void) const
{
const uint32_t ranks = shape().rank();
int32_t elements = 1;
for (uint32_t rank = 0; rank < ranks; rank++)
{
elements *= shape().dim(rank);
}
DataType type = typeInfo().type();
size_t element_size = sizeOfDataType(type);
// Value of type is matched with OperandCode enum in NeuralNetworks.h
return element_size * elements;
}
void Operand::appendUse(const OperationIndex &idx) { _uses.append(idx); }
void Operand::removeUse(const OperationIndex &idx) { _uses.remove(idx); }
void Operand::appendDef(const OperationIndex &idx)
{
assert(!isConstant());
assert(_def.size() == 0);
_def.append(idx);
}
void Operand::removeDef(const OperationIndex &idx)
{
assert(_def.contains(idx));
_def.remove(idx);
}
void Operand::parent_info(std::unique_ptr<operand::ParentInfo> &&parent_info)
{
_parent_info = std::move(parent_info);
}
const operand::ParentInfo *Operand::parent_info() const { return _parent_info.get(); }
operand::ParentInfo *Operand::parent_info() { return _parent_info.get(); }
} // namespace ir
} // namespace neurun
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