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/*
* Copyright (c) 2019 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 <cker/operation/Pad.h>
#include "OperationUtil.h"
#include "interp/Registration.h"
#include "ir/operation/Pad.h"
namespace onert
{
namespace interp
{
namespace
{
void preparePad(ExecEnv *env, const ir::Operation &node)
{
const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT);
const auto output_index = node.getOutputs().at(0);
const auto input_tensor = env->tensorAt(input_index);
const auto output_info = env->graph().operands().at(output_index).info();
// Check shape and type lhs is same with rhs
// TODO Util function to compare TensorInfo
if (output_info.total_size() == 0)
{
throw std::runtime_error{"Interp(Pad): NYI unspecified output shape"};
}
else
{
env->allocateIfNeeded(output_index, output_info);
}
const auto output_tensor = env->tensorAt(output_index);
if (input_tensor->data_type() != output_tensor->data_type())
{
throw std::runtime_error{"Interp(Pad): Invalid output type"};
}
}
void invoke(const ITensor *input_tensor, const ITensor *pad_tensor, const ITensor *output_tensor)
{
const auto input_buffer = input_tensor->bufferRO();
const auto pad_buffer = pad_tensor->bufferRO();
auto output_buffer = output_tensor->buffer();
int32_t pad_rank = pad_tensor->dimension(0);
const auto cker_input_shape = convertShape(input_tensor->tensorInfo().shape());
const auto cker_output_shape = convertShape(output_tensor->tensorInfo().shape());
const float *input_ptr = reinterpret_cast<const float *>(input_buffer);
const int32_t *pad_ptr = reinterpret_cast<const int32_t *>(pad_buffer);
float *output_ptr = reinterpret_cast<float *>(output_buffer);
nnfw::cker::Pad<float>(pad_ptr, pad_rank, cker_input_shape, input_ptr, cker_output_shape,
output_ptr, nullptr);
}
void invokePad(const ExecEnv *env, const ir::Operation &node)
{
const auto input_index = node.getInputs().at(ir::operation::Pad::INPUT);
const auto pad_index = node.getInputs().at(ir::operation::Pad::PAD);
const auto output_index = node.getOutputs().at(0);
const auto input_tensor = env->tensorAt(input_index);
const auto pad_tensor = env->tensorAt(pad_index);
const auto output_tensor = env->tensorAt(output_index);
const auto data_type = input_tensor->data_type();
if (data_type == ir::DataType::FLOAT32)
{
invoke(input_tensor, pad_tensor, output_tensor);
}
else
{
throw std::runtime_error{"Interp(Pad): NYI - Unsupported data type"};
}
}
} // namespace
OpKernel *getPad()
{
static OpKernel kernel = {preparePad, invokePad};
return &kernel;
}
} // namespace interp
} // namespace onert
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