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/*
* Copyright (c) 2016-2019 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.
*/
#include "arm_compute/runtime/NEON/functions/NEConvolution.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/kernels/NEConvolutionKernel.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "arm_compute/runtime/TensorAllocator.h"
#include "support/ToolchainSupport.h"
#include <array>
#include <utility>
using namespace arm_compute;
void NEConvolution3x3::configure(ITensor *input, ITensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
{
auto k = arm_compute::support::cpp14::make_unique<NEConvolution3x3Kernel>();
k->configure(input, output, conv, scale, border_mode == BorderMode::UNDEFINED);
_kernel = std::move(k);
_border_handler.configure(input, _kernel->border_size(), border_mode, PixelValue(constant_border_value));
}
template <unsigned int matrix_size>
NEConvolutionSquare<matrix_size>::NEConvolutionSquare(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _tmp(), _is_separable(false), _kernel_hor(), _kernel_vert(), _kernel(), _border_handler()
{
}
template <unsigned int matrix_size>
void NEConvolutionSquare<matrix_size>::configure(ITensor *input, ITensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode,
uint8_t constant_border_value)
{
ARM_COMPUTE_ERROR_ON(conv == nullptr);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U8, DataType::S16);
std::array<int16_t, matrix_size> conv_col{ { 0 } };
std::array<int16_t, matrix_size> conv_row{ { 0 } };
_is_separable = separate_matrix(conv, conv_col.data(), conv_row.data(), matrix_size);
if(_is_separable)
{
DataType intermediate_type = DataType::UNKNOWN;
std::tie(std::ignore, intermediate_type) = data_type_for_convolution(conv_col.data(), conv_row.data(), matrix_size);
_tmp.allocator()->init(TensorInfo(input->info()->tensor_shape(), 1, intermediate_type));
// Manage intermediate buffers
_memory_group.manage(&_tmp);
// Calculate scale
if(scale == 0)
{
scale = calculate_matrix_scale(conv, matrix_size);
}
_kernel_hor.configure(input, &_tmp, conv_row.data(), border_mode == BorderMode::UNDEFINED);
_kernel_vert.configure(&_tmp, output, conv_col.data(), scale, border_mode == BorderMode::UNDEFINED);
_tmp.allocator()->allocate();
_border_handler.configure(input, _kernel_hor.border_size(), border_mode, PixelValue(constant_border_value));
}
else
{
_kernel.configure(input, output, conv, scale, border_mode == BorderMode::UNDEFINED);
_border_handler.configure(input, _kernel.border_size(), border_mode, PixelValue(constant_border_value));
}
}
template <unsigned int matrix_size>
void NEConvolutionSquare<matrix_size>::run()
{
NEScheduler::get().schedule(&_border_handler, Window::DimZ);
if(_is_separable)
{
MemoryGroupResourceScope scope_mg(_memory_group);
NEScheduler::get().schedule(&_kernel_hor, Window::DimY);
NEScheduler::get().schedule(&_kernel_vert, Window::DimY);
}
else
{
NEScheduler::get().schedule(&_kernel, Window::DimY);
}
}
template class arm_compute::NEConvolutionSquare<5>;
template class arm_compute::NEConvolutionSquare<7>;
template class arm_compute::NEConvolutionSquare<9>;
void NEConvolutionRectangle::configure(ITensor *input, ITensor *output, const int16_t *conv, uint32_t rows, uint32_t cols, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
{
auto k = arm_compute::support::cpp14::make_unique<NEConvolutionRectangleKernel>();
k->configure(input, output, conv, rows, cols, scale, border_mode == BorderMode::UNDEFINED);
_kernel = std::move(k);
_border_handler.configure(input, _kernel->border_size(), border_mode, PixelValue(constant_border_value));
}
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