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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_ALEXNET_H__
#define __ARM_COMPUTE_TEST_BENCHMARK_ALEXNET_H__
#include "TensorLibrary.h"
#include "Utils.h"
#include "benchmark/Profiler.h"
#include "benchmark/WallClockTimer.h"
#include "model_objects/AlexNet.h"
using namespace arm_compute;
using namespace arm_compute::test;
using namespace arm_compute::test::benchmark;
namespace arm_compute
{
namespace test
{
namespace benchmark
{
template <typename ITensorType,
typename TensorType,
typename SubTensorType,
typename Accessor,
typename ActivationLayerFunction,
typename ConvolutionLayerFunction,
typename FullyConnectedLayerFunction,
typename NormalizationLayerFunction,
typename PoolingLayerFunction,
typename SoftmaxLayerFunction,
DataType dt = DataType::F32>
class AlexNetFixture : public ::benchmark::Fixture
{
public:
void SetUp(::benchmark::State &state) override
{
profiler.add(std::make_shared<WallClockTimer>());
const unsigned int batches = static_cast<unsigned int>(state.range(0));
const bool weights_transposed = true;
network.init_weights(batches, weights_transposed);
network.build();
network.allocate();
network.fill_random();
}
void TearDown(::benchmark::State &state) override
{
profiler.submit(state);
network.clear();
}
Profiler profiler{};
model_objects::AlexNet<ITensorType,
TensorType,
SubTensorType,
Accessor,
ActivationLayerFunction,
ConvolutionLayerFunction,
FullyConnectedLayerFunction,
NormalizationLayerFunction,
PoolingLayerFunction,
SoftmaxLayerFunction,
dt>
network{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif //__ARM_COMPUTE_TEST_BENCHMARK_ALEXNET_H__
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