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/*
* Copyright (c) 2017-2020 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_SCALE_FIXTURE
#define ARM_COMPUTE_TEST_SCALE_FIXTURE
#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
{
template <typename TensorType, typename Function, typename Accessor>
class ScaleFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape shape, DataType data_type, DataLayout data_layout, InterpolationPolicy policy, BorderMode border_mode, SamplingPolicy sampling_policy)
{
constexpr float max_width = 8192.0f;
constexpr float max_height = 6384.0f;
// Change shape in case of NHWC.
if(data_layout == DataLayout::NHWC)
{
permute(shape, PermutationVector(2U, 0U, 1U));
}
std::mt19937 generator(library->seed());
std::uniform_real_distribution<float> distribution_float(0.25f, 3.0f);
float scale_x = distribution_float(generator);
float scale_y = distribution_float(generator);
scale_x = ((shape.x() * scale_x) > max_width) ? (max_width / shape.x()) : scale_x;
scale_y = ((shape.y() * scale_y) > max_height) ? (max_height / shape.y()) : scale_y;
std::uniform_int_distribution<uint8_t> distribution_u8(0, 255);
uint8_t constant_border_value = static_cast<uint8_t>(distribution_u8(generator));
const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
TensorShape shape_scaled(shape);
shape_scaled.set(idx_width, shape[idx_width] * scale_x);
shape_scaled.set(idx_height, shape[idx_height] * scale_y);
// Create tensors
src = create_tensor<TensorType>(shape, data_type);
dst = create_tensor<TensorType>(shape_scaled, data_type);
// Create and configure function
scale_func.configure(&src, &dst, ScaleKernelInfo{ policy, border_mode, constant_border_value, sampling_policy });
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
}
void run()
{
scale_func.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 scale_func{};
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_SCALE_FIXTURE */
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