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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 <OperationsUtils.h>
#include <arm_compute/core/TensorShape.h>
#include <arm_compute/core/TensorInfo.h>
#include "../IO_accessor.h"
#include "../shape.h"
#include "../NEUniqueTensor.h"
#include <cassert>
namespace nnfw {
namespace kernel {
namespace acl {
namespace neon {
bool maxPoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height,
int32_t filter_width, int32_t filter_height,
int32_t activation,
float* outputData, const nnfw::rt::Shape& outputShape)
{
arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
arm_compute::PadStrideInfo pad_info = arm_compute::PadStrideInfo(stride_width, stride_height,
padding_left, padding_right,
padding_top, padding_bottom,
arm_compute::DimensionRoundingType::FLOOR);
arm_compute::PoolingLayerInfo maxpool_info = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::MAX,
arm_compute::Size2D(filter_width,filter_height),
pad_info, false);
NEUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
NEUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
auto pool_f = std::make_shared<arm_compute::NEPoolingLayer>();
pool_f->configure(input.ptr(), output.ptr(), maxpool_info);
fns.emplace_back(pool_f);
util::insertFusedActivationLayer<NEUniqueTensor, arm_compute::NEActivationLayer>(output, activation, fns);
input.allocate();
output.allocate();
TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
for (const auto &fn : fns)
{
fn->run();
}
TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
return true;
}
bool averagePoolFloat32(const float* inputData, const nnfw::rt::Shape& inputShape,
int32_t padding_left, int32_t padding_right,
int32_t padding_top, int32_t padding_bottom,
int32_t stride_width, int32_t stride_height,
int32_t filter_width, int32_t filter_height,
int32_t activation,
float* outputData, const nnfw::rt::Shape& outputShape)
{
arm_compute::TensorShape input_shape = util::fromNNShape(inputShape);
arm_compute::TensorShape output_shape = util::fromNNShape(outputShape);
std::vector<std::shared_ptr<arm_compute::IFunction>> fns;
arm_compute::PadStrideInfo pad_info = arm_compute::PadStrideInfo(stride_width, stride_height,
padding_left, padding_right,
padding_top, padding_bottom,
arm_compute::DimensionRoundingType::FLOOR);
arm_compute::PoolingLayerInfo pool_info = arm_compute::PoolingLayerInfo(arm_compute::PoolingType::AVG,
arm_compute::Size2D(filter_width,filter_height),
pad_info, true);
NEUniqueTensor input(arm_compute::TensorInfo(input_shape, arm_compute::Format::F32));
NEUniqueTensor output(arm_compute::TensorInfo(output_shape, arm_compute::Format::F32));
auto pool_f = std::make_shared<arm_compute::NEPoolingLayer>();
pool_f->configure(input.ptr(), output.ptr(), pool_info);
fns.emplace_back(pool_f);
util::insertFusedActivationLayer<NEUniqueTensor, arm_compute::NEActivationLayer>(output, activation, fns);
input.allocate();
output.allocate();
TensorAccess<InputAccessor>(input.ref(), inputData, inputShape);
for (const auto &fn : fns)
{
fn->run();
}
TensorAccess<OutputAccessor>(output.ref(), outputData, outputShape);
return true;
}
} // namespace neon
} // namespace acl
} // namespace kernel
} // namespace nnfw
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