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/*
* Copyright (c) 2016-2020 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 "arm_compute/runtime/CL/functions/CLConvolution.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/kernels/CLConvolutionKernel.h"
#include "arm_compute/core/Error.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/CL/CLScheduler.h"
#include "arm_compute/runtime/ITensorAllocator.h"
#include "support/MemorySupport.h"
#include <utility>
using namespace arm_compute;
void CLConvolution3x3::configure(ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, conv, scale, border_mode, constant_border_value);
}
void CLConvolution3x3::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode,
uint8_t constant_border_value)
{
auto k = arm_compute::support::cpp14::make_unique<CLConvolution3x3Kernel>();
k->configure(compile_context, input, output, conv, scale, border_mode == BorderMode::UNDEFINED);
_kernel = std::move(k);
_border_handler.configure(compile_context, input, _kernel->border_size(), border_mode, PixelValue(constant_border_value));
}
template <unsigned int matrix_size>
CLConvolutionSquare<matrix_size>::CLConvolutionSquare(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 CLConvolutionSquare<matrix_size>::configure(ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode,
uint8_t constant_border_value)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, conv, scale, border_mode, constant_border_value);
}
template <unsigned int matrix_size>
void CLConvolutionSquare<matrix_size>::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t scale, BorderMode border_mode,
uint8_t constant_border_value)
{
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8);
ARM_COMPUTE_ERROR_ON(conv == nullptr);
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)
{
std::pair<DataType, DataType> type_pair = data_type_for_convolution(conv_col.data(), conv_row.data(), matrix_size);
_tmp.allocator()->init(TensorInfo(input->info()->tensor_shape(), 1, type_pair.first));
// Manage intermediate buffers
_memory_group.manage(&_tmp);
if(scale == 0)
{
scale = calculate_matrix_scale(conv, matrix_size);
}
_kernel_hor.configure(compile_context, input, &_tmp, conv_row.data(), border_mode == BorderMode::UNDEFINED);
_kernel_vert.configure(compile_context, &_tmp, output, conv_col.data(), scale, border_mode == BorderMode::UNDEFINED, type_pair.second);
_border_handler.configure(compile_context, input, _kernel_hor.border_size(), border_mode, PixelValue(constant_border_value));
// Allocate intermediate buffer
_tmp.allocator()->allocate();
}
else
{
_kernel.configure(compile_context, input, output, conv, scale, border_mode == BorderMode::UNDEFINED);
_border_handler.configure(compile_context, input, _kernel.border_size(), border_mode, PixelValue(constant_border_value));
}
}
template <unsigned int matrix_size>
void CLConvolutionSquare<matrix_size>::run()
{
CLScheduler::get().enqueue(_border_handler);
if(_is_separable)
{
MemoryGroupResourceScope scope_mg(_memory_group);
CLScheduler::get().enqueue(_kernel_hor, false);
CLScheduler::get().enqueue(_kernel_vert);
}
else
{
CLScheduler::get().enqueue(_kernel);
}
}
template class arm_compute::CLConvolutionSquare<5>;
template class arm_compute::CLConvolutionSquare<7>;
template class arm_compute::CLConvolutionSquare<9>;
void CLConvolutionRectangle::configure(ICLTensor *input, ICLTensor *output, const int16_t *conv, uint32_t rows, uint32_t cols, uint32_t scale, BorderMode border_mode, uint8_t constant_border_value)
{
configure(CLKernelLibrary::get().get_compile_context(), input, output, conv, rows, cols, scale, border_mode, constant_border_value);
}
void CLConvolutionRectangle::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *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<CLConvolutionRectangleKernel>();
k->configure(compile_context, input, output, conv, rows, cols, scale, border_mode == BorderMode::UNDEFINED);
_kernel = std::move(k);
_border_handler.configure(compile_context, input, _kernel->border_size(), border_mode, PixelValue(constant_border_value));
}
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