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/*
* Copyright (c) 2017-2018 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_GEMMLOWPFIXTURE
#define ARM_COMPUTE_TEST_GEMMLOWPFIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "tests/Globals.h"
#include "tests/Utils.h"
#include "tests/framework/Fixture.h"
namespace arm_compute
{
namespace test
{
namespace benchmark
{
/** Fixture that can be used for NEON and CL */
template <typename TensorType, typename Function, typename Accessor>
class GEMMLowpMatrixMultiplyCoreFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape_a, TensorShape shape_b, TensorShape shape_c, TensorShape shape_dst, float alpha, float beta)
{
// TODO (COMPMID-717): The interface used for GEMMLowp is the same one used for GEMM in order to re-use the datasets
// However the interface for both GEMM and GEMMLowp should be reworked in order to accepts only the 3 dimensions M, N and K
ARM_COMPUTE_UNUSED(shape_c);
ARM_COMPUTE_UNUSED(alpha);
ARM_COMPUTE_UNUSED(beta);
// Note: The offsets for matrix A and matrix B are set to 0 in order to skip the computation for the offset contribution
// Create tensors
a = create_tensor<TensorType>(shape_a, DataType::QASYMM8, 1, QuantizationInfo(1.0f / 255.0f, 0));
b = create_tensor<TensorType>(shape_b, DataType::QASYMM8, 1, QuantizationInfo(1.0f / 255.0f, 0));
c = create_tensor<TensorType>(shape_dst, DataType::S32, 1, QuantizationInfo(1.0f / 255.0f, 0));
// Create and configure function
gemmlowp.configure(&a, &b, &c);
// Allocate tensors
a.allocator()->allocate();
b.allocator()->allocate();
c.allocator()->allocate();
}
void run()
{
gemmlowp.run();
}
void sync()
{
sync_if_necessary<TensorType>();
sync_tensor_if_necessary<TensorType>(c);
}
void teardown()
{
a.allocator()->free();
b.allocator()->free();
c.allocator()->free();
}
private:
TensorType a{};
TensorType b{};
TensorType c{};
Function gemmlowp{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_GEMMLOWPFIXTURE */
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