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/*
* Copyright (c) 2017 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef __ARM_COMPUTE_TEST_BENCHMARK_NORMALIZATION_LAYER_H__
#define __ARM_COMPUTE_TEST_BENCHMARK_NORMALIZATION_LAYER_H__
#include "TensorLibrary.h"
#include "Utils.h"
#include "dataset/NormalizationLayerDataset.h"
#include <memory>
#include <string>
using namespace arm_compute;
using namespace arm_compute::test;
using namespace arm_compute::test::benchmark;
namespace arm_compute
{
namespace test
{
namespace benchmark
{
template <typename DataSet, typename TensorType, typename Accessor, typename Function, DataType dt = DataType::F32>
class NormalizationLayer : public ::benchmark::Fixture
{
public:
void SetUp(::benchmark::State &state) override
{
profiler.add(std::make_shared<WallClockTimer>());
const NormalizationLayerDataObject norm_obj = *(DataSet().begin() + state.range(0));
// Set batched in source and destination shapes
const unsigned int batches = state.range(1);
const unsigned int fixed_point_position = 4;
TensorShape shape = norm_obj.shape;
shape.set(shape.num_dimensions(), batches);
// Create tensors
src = create_tensor(shape, dt, 1, fixed_point_position);
dst = create_tensor(shape, dt, 1, fixed_point_position);
// Create and configure function
norm_layer = std::unique_ptr<Function>(new Function());
norm_layer->configure(&src, &dst, norm_obj.info);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
// Fill tensors
library->fill_tensor_uniform(Accessor(src), 0);
}
void TearDown(::benchmark::State &state) override
{
norm_layer.reset();
src.allocator()->free();
dst.allocator()->free();
profiler.submit(state);
}
std::unique_ptr<Function> norm_layer{ nullptr };
Profiler profiler{};
private:
TensorType src{};
TensorType dst{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif //__ARM_COMPUTE_TEST_BENCHMARK_NORMALIZATION_LAYER_H__
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