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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.
*/
#include "Derivative.h"
#include "Utils.h"
#include "tests/Types.h"
#include <array>
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace reference
{
namespace
{
const std::array<int8_t, 9> derivative_3_x{ { 0, 0, 0, -1, 0, 1, 0, 0, 0 } };
const std::array<int8_t, 9> derivative_3_y{ { 0, -1, 0, 0, 0, 0, 0, 1, 0 } };
template <typename T>
struct data_type;
template <>
struct data_type<int16_t>
{
const static DataType value = DataType::S16;
};
} // namespace
template <typename T, typename U>
std::pair<SimpleTensor<T>, SimpleTensor<T>> derivative(const SimpleTensor<U> &src, BorderMode border_mode, uint8_t constant_border_value, GradientDimension gradient_dimension)
{
const unsigned int filter_size = 3;
SimpleTensor<T> dst_x(src.shape(), data_type<T>::value, src.num_channels());
SimpleTensor<T> dst_y(src.shape(), data_type<T>::value, src.num_channels());
ValidRegion valid_region = shape_to_valid_region(src.shape(), border_mode == BorderMode::UNDEFINED, BorderSize(filter_size / 2));
const uint32_t num_elements = src.num_elements();
#if defined(_OPENMP)
#pragma omp parallel for
#endif /* _OPENMP */
for(uint32_t i = 0; i < num_elements; ++i)
{
Coordinates coord = index2coord(src.shape(), i);
if(!is_in_valid_region(valid_region, coord))
{
continue;
}
switch(gradient_dimension)
{
case GradientDimension::GRAD_X:
apply_2d_spatial_filter(coord, src, dst_x, TensorShape{ filter_size, filter_size }, derivative_3_x.data(), 1.f, border_mode,
constant_border_value);
break;
case GradientDimension::GRAD_Y:
apply_2d_spatial_filter(coord, src, dst_y, TensorShape{ filter_size, filter_size }, derivative_3_y.data(), 1.f, border_mode,
constant_border_value);
break;
case GradientDimension::GRAD_XY:
apply_2d_spatial_filter(coord, src, dst_x, TensorShape{ filter_size, filter_size }, derivative_3_x.data(), 1.f, border_mode,
constant_border_value);
apply_2d_spatial_filter(coord, src, dst_y, TensorShape{ filter_size, filter_size }, derivative_3_y.data(), 1.f, border_mode,
constant_border_value);
break;
default:
ARM_COMPUTE_ERROR("Gradient dimension not supported");
}
}
return std::make_pair(dst_x, dst_y);
}
template std::pair<SimpleTensor<int16_t>, SimpleTensor<int16_t>> derivative(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value,
GradientDimension gradient_dimension);
} // namespace reference
} // namespace validation
} // namespace test
} // namespace arm_compute
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