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/*
* Copyright (c) 2019 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_PADLAYERFIXTURE
#define ARM_COMPUTE_TEST_PADLAYERFIXTURE
#include "arm_compute/core/TensorShape.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.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 Accessor, typename Function, typename T>
class PaddingFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, const PaddingList &paddings, const PaddingMode mode)
{
PaddingList clamped_padding = paddings;
if(mode != PaddingMode::CONSTANT)
{
// Clamp padding to prevent applying more than is possible.
for(uint32_t i = 0; i < paddings.size(); ++i)
{
if(mode == PaddingMode::REFLECT)
{
clamped_padding[i].first = std::min(static_cast<uint64_t>(paddings[i].first), static_cast<uint64_t>(shape[i] - 1));
clamped_padding[i].second = std::min(static_cast<uint64_t>(paddings[i].second), static_cast<uint64_t>(shape[i] - 1));
}
else
{
clamped_padding[i].first = std::min(static_cast<uint64_t>(paddings[i].first), static_cast<uint64_t>(shape[i]));
clamped_padding[i].second = std::min(static_cast<uint64_t>(paddings[i].second), static_cast<uint64_t>(shape[i]));
}
}
}
const PixelValue const_value = PixelValue(static_cast<T>(0));
TensorShape output_shape = arm_compute::misc::shape_calculator::compute_padded_shape(shape, paddings);
// Create tensors
src = create_tensor<TensorType>(shape, data_type);
dst = create_tensor<TensorType>(output_shape, data_type);
// Create and configure function
pad_layer.configure(&src, &dst, paddings, const_value, mode);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
}
void run()
{
pad_layer.run();
}
void sync()
{
sync_if_necessary<TensorType>();
sync_tensor_if_necessary<TensorType>(dst);
}
void teardown()
{
src.allocator()->free();
dst.allocator()->free();
}
private:
TensorType src{};
TensorType dst{};
Function pad_layer{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_PADLAYERFIXTURE */
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