OperationUtils.h

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/*
 * Copyright (c) 2023 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.
 */
 
#ifndef __ONERT_BACKEND_TRAIN_OPS_OPERATION_UTILS_H__
#define __ONERT_BACKEND_TRAIN_OPS_OPERATION_UTILS_H__
 
#include <ops/OperationUtils.h>
#include <cker/train/Types.h>
#include <ir/train/LossInfo.h>
 
namespace onert
{
namespace backend
{
namespace train
{
namespace ops
{
 
using OperandType = onert::ir::DataType;
using cpu::ops::getBuffer;
using cpu::ops::getPaddingType;
using cpu::ops::getNumberOfDimensions;
using cpu::ops::getNumberOfElements;
using cpu::ops::getSizeOfDimension;
 
nnfw::cker::Shape getShape(const IPortableTensor *tensor);
 
const IPortableTensor *backpropActivation(const ir::Activation &activation,
                                          const IPortableTensor *output,
                                          const IPortableTensor *input_backprop,
                                          IPortableTensor *output_backprop);
 
void biasGrad(const IPortableTensor *input_backprop, IPortableTensor *bias_grad);
 
nnfw::cker::train::LossReductionType convertLossReductionType(ir::train::LossReductionType type);
 
} // namespace ops
} // namespace train
} // namespace backend
} // namespace onert
 
#endif // __ONERT_BACKEND_TRAIN_OPS_OPERATION_UTILS_H__