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/*
* Copyright (c) 2018-2019 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_CONVOLUTIONFIXTURE
#define ARM_COMPUTE_TEST_CONVOLUTIONFIXTURE
#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
{
/** Parent fixture that can be used for NEON and CL */
template <typename TensorType, typename Function, typename Accessor>
class ConvolutionFixture : public framework::Fixture
{
public:
template <typename...>
void setup(TensorShape src_shape, DataType output_data_type, BorderMode border_mode, unsigned int width, unsigned int height, bool is_separable = false)
{
std::mt19937 gen(library->seed());
const uint8_t constant_border_value = 0;
// Generate random scale value between 1 and 255.
std::uniform_int_distribution<uint8_t> distribution_scale(1, 255);
const uint32_t scale = distribution_scale(gen);
ARM_COMPUTE_ERROR_ON(3 != width && 5 != width && 7 != width && 9 != width);
ARM_COMPUTE_ERROR_ON(3 != height && 5 != height && 7 != height && 9 != height);
std::vector<int16_t> conv(width * height);
_width = width;
_height = height;
if(is_separable)
{
init_separable_conv(conv.data(), width, height, seed);
}
else
{
init_conv(conv.data(), width, height, seed);
}
// Create tensors
src = create_tensor<TensorType>(src_shape, DataType::U8);
dst = create_tensor<TensorType>(src_shape, output_data_type);
// Configure function
configure_target(src, dst, conv.data(), scale, border_mode, constant_border_value);
// Allocate tensors
src.allocator()->allocate();
dst.allocator()->allocate();
// Fill tensors
library->fill_tensor_uniform(Accessor(src), 0);
library->fill_tensor_uniform(Accessor(dst), 1);
}
void run()
{
convolution_func.run();
}
void sync()
{
sync_if_necessary<TensorType>();
sync_tensor_if_necessary<TensorType>(dst);
}
protected:
virtual void configure_target(TensorType &src, TensorType &dst, const int16_t *conv, uint32_t scale,
BorderMode border_mode, uint8_t border_value) = 0;
protected:
unsigned int _width{};
unsigned int _height{};
Function convolution_func{};
private:
const std::random_device::result_type seed = 0;
TensorType src{};
TensorType dst{};
};
/** Child fixture used for square convolutions */
template <typename TensorType, typename Function, typename Accessor>
class ConvolutionSquareFixture : public ConvolutionFixture<TensorType, Function, Accessor>
{
public:
template <typename...>
void setup(TensorShape src_shape, DataType output_data_type, BorderMode border_mode, unsigned int width)
{
ConvolutionFixture<TensorType, Function, Accessor>::setup(src_shape, output_data_type, border_mode, width, width);
}
protected:
void configure_target(TensorType &src, TensorType &dst, const int16_t *conv, uint32_t scale,
BorderMode border_mode, uint8_t constant_border_value)
{
this->convolution_func.configure(&src, &dst, conv, scale, border_mode, constant_border_value);
}
};
/** Child fixture used for rectangular convolutions */
template <typename TensorType, typename Function, typename Accessor>
class ConvolutionRectangleFixture : public ConvolutionFixture<TensorType, Function, Accessor>
{
public:
template <typename...>
void setup(TensorShape src_shape, DataType output_data_type, BorderMode border_mode, unsigned int width, unsigned int height)
{
ConvolutionFixture<TensorType, Function, Accessor>::setup(src_shape, output_data_type, border_mode, width, height);
}
protected:
void configure_target(TensorType &src, TensorType &dst, const int16_t *conv, uint32_t scale,
BorderMode border_mode, uint8_t constant_border_value)
{
this->convolution_func.configure(&src, &dst, conv, this->_width, this->_height, scale, border_mode, constant_border_value);
}
};
/** Child fixture used for separable convolutions */
template <typename TensorType, typename Function, typename Accessor>
class ConvolutionSeperableFixture : public ConvolutionFixture<TensorType, Function, Accessor>
{
public:
template <typename...>
void setup(TensorShape src_shape, DataType output_data_type, BorderMode border_mode, unsigned int width)
{
ConvolutionFixture<TensorType, Function, Accessor>::setup(src_shape, output_data_type, border_mode, width, width, true);
}
protected:
void configure_target(TensorType &src, TensorType &dst, const int16_t *conv, uint32_t scale,
BorderMode border_mode, uint8_t constant_border_value)
{
this->convolution_func.configure(&src, &dst, conv, scale, border_mode, constant_border_value);
}
};
} // namespace benchmark
} // namespace test
} // namespace arm_compute
#endif /* ARM_COMPUTE_TEST_CONVOLUTIONFIXTURE */
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