107 lines
4.0 KiB
C++
107 lines
4.0 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "Derivative.h"
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#include "Utils.h"
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#include "tests/Types.h"
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#include <array>
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace reference
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{
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namespace
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{
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const std::array<int8_t, 9> derivative_3_x{ { 0, 0, 0, -1, 0, 1, 0, 0, 0 } };
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const std::array<int8_t, 9> derivative_3_y{ { 0, -1, 0, 0, 0, 0, 0, 1, 0 } };
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template <typename T>
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struct data_type;
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template <>
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struct data_type<int16_t>
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{
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const static DataType value = DataType::S16;
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};
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} // namespace
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template <typename T, typename U>
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std::pair<SimpleTensor<T>, SimpleTensor<T>> derivative(const SimpleTensor<U> &src, BorderMode border_mode, uint8_t constant_border_value, GradientDimension gradient_dimension)
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{
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const unsigned int filter_size = 3;
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SimpleTensor<T> dst_x(src.shape(), data_type<T>::value, src.num_channels());
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SimpleTensor<T> dst_y(src.shape(), data_type<T>::value, src.num_channels());
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ValidRegion valid_region = shape_to_valid_region(src.shape(), border_mode == BorderMode::UNDEFINED, BorderSize(filter_size / 2));
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const uint32_t num_elements = src.num_elements();
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#if defined(_OPENMP)
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#pragma omp parallel for
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#endif /* _OPENMP */
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for(uint32_t i = 0; i < num_elements; ++i)
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{
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Coordinates coord = index2coord(src.shape(), i);
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if(!is_in_valid_region(valid_region, coord))
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{
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continue;
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}
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switch(gradient_dimension)
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{
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case GradientDimension::GRAD_X:
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apply_2d_spatial_filter(coord, src, dst_x, TensorShape{ filter_size, filter_size }, derivative_3_x.data(), 1.f, border_mode,
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constant_border_value);
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break;
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case GradientDimension::GRAD_Y:
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apply_2d_spatial_filter(coord, src, dst_y, TensorShape{ filter_size, filter_size }, derivative_3_y.data(), 1.f, border_mode,
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constant_border_value);
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break;
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case GradientDimension::GRAD_XY:
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apply_2d_spatial_filter(coord, src, dst_x, TensorShape{ filter_size, filter_size }, derivative_3_x.data(), 1.f, border_mode,
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constant_border_value);
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apply_2d_spatial_filter(coord, src, dst_y, TensorShape{ filter_size, filter_size }, derivative_3_y.data(), 1.f, border_mode,
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constant_border_value);
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break;
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default:
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ARM_COMPUTE_ERROR("Gradient dimension not supported");
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}
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}
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return std::make_pair(dst_x, dst_y);
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}
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template std::pair<SimpleTensor<int16_t>, SimpleTensor<int16_t>> derivative(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value,
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GradientDimension gradient_dimension);
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} // namespace reference
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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