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/*
* Copyright (c) 2016, 2017 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/NEDeconvolutionLayerUpsample.h"
#include "arm_compute/core/Coordinates.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/kernels/NEDeconvolutionLayerUpsampleKernel.h"
#include "arm_compute/core/PixelValue.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Window.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include "arm_compute/runtime/TensorAllocator.h"
#include "support/ToolchainSupport.h"
#include <cmath>
#include <cstddef>
#include <utility>
using namespace arm_compute;
namespace
{
inline void precompute_offsets(ITensor *offsets, float wr, size_t input_element_size, const std::pair<unsigned int, unsigned int> &a,
const std::pair<unsigned int, unsigned int> &iz, const PadStrideInfo &info)
{
ARM_COMPUTE_ERROR_ON(nullptr == offsets);
Window win;
const int padx = info.pad().first;
const int pady = info.pad().second;
const int ax = a.first;
const int ay = a.second;
const int offset_width = offsets->info()->dimension(0);
const int offset_height = offsets->info()->dimension(1);
// The values of ax and ay denote the number of ZEROS to be added on the top and right inner border of the image.
// Step value along the XY axis will depend on the number of zeros to be inserted between samples (number of zeros + 1).
// Pre-compute the X offset, Y's stride is unknown at this point so we can't precompute Y's offsets
for(int yi = ay; yi < (offset_height - pady); yi += (1 + iz.second))
{
for(int xi = padx; xi < (offset_width - ax); xi += (1 + iz.first))
{
int *ptr = reinterpret_cast<int *>(offsets->ptr_to_element(Coordinates(xi, yi)));
const size_t in_xi = (xi + 0.5f) * wr;
*reinterpret_cast<int32_t *>(ptr) = in_xi * input_element_size;
}
}
}
} // namespace
NEDeconvolutionLayerUpsample::NEDeconvolutionLayerUpsample(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
_offsets(),
_border_handler(),
_upsample()
{
}
void NEDeconvolutionLayerUpsample::configure(ITensor *input, ITensor *output, const std::pair<unsigned int, unsigned int> &a,
const std::pair<unsigned int, unsigned int> &iz, const PadStrideInfo &info)
{
ARM_COMPUTE_ERROR_ON(nullptr == input);
ARM_COMPUTE_ERROR_ON(nullptr == output);
for(size_t i = 2; i < Coordinates::num_max_dimensions; ++i)
{
ARM_COMPUTE_ERROR_ON(input->info()->dimension(i) != output->info()->dimension(i));
}
// Get the tensor shape
const TensorShape shape(output->info()->dimension(0), output->info()->dimension(1));
// Compute the ratio between source width/height and destination width/height
const auto wr = static_cast<float>(input->info()->dimension(0)) / static_cast<float>(output->info()->dimension(0));
const auto hr = static_cast<float>(input->info()->dimension(1)) / static_cast<float>(output->info()->dimension(1));
ARM_COMPUTE_UNUSED(hr);
// Get the element size of the input image
const size_t input_element_size = input->info()->element_size();
TensorInfo tensor_info_offsets(shape, Format::S32);
_offsets.allocator()->init(tensor_info_offsets);
_upsample.configure(input, &_offsets, output);
// Allocate once the configure methods have been called
_offsets.allocator()->allocate();
// Pre-compute offsets for nearest interpolation
std::fill_n(reinterpret_cast<int32_t *>(_offsets.buffer()), _offsets.info()->total_size() / sizeof(int32_t), -1 * input_element_size);
precompute_offsets(&_offsets, wr, input_element_size, a, iz, info);
_border_handler.configure(input, _upsample.border_size(), BorderMode::CONSTANT, PixelValue(0));
}
void NEDeconvolutionLayerUpsample::run()
{
NEScheduler::get().schedule(&_border_handler, Window::DimZ);
_memory_group.acquire();
NEScheduler::get().schedule(&_upsample, Window::DimY);
_memory_group.release();
}
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