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/*
* Copyright (c) 2017, 2018 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/NEDeconvolutionLayer.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
using namespace arm_compute;
using namespace arm_compute::misc::shape_calculator;
NEDeconvolutionLayer::NEDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
_conv_f(),
_scaled_output(),
_input(nullptr),
_info(),
_inner_border()
{
}
void NEDeconvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != weights->info()->dimension(1));
ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 1 && weights->info()->dimension(0) != 3 && weights->info()->dimension(0) != 5);
_input = input;
_info = info;
_inner_border = std::make_pair(inner_border_right, inner_border_top);
const unsigned int stride_x = info.stride().first;
const unsigned int stride_y = info.stride().second;
auto out_dims = deconvolution_output_dimensions(input->info()->dimension(0), input->info()->dimension(1), weights->info()->dimension(0), weights->info()->dimension(1),
info.pad().first, info.pad().second, inner_border_right, inner_border_top, stride_x, stride_y);
const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
ARM_COMPUTE_ERROR_ON_MSG(output->info()->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
_memory_group.manage(&_scaled_output);
// configure scale function
// Init and allocate intermmidiate tensor for output, same size as input but the first two axis are the same as the output tensor
const TensorInfo scale_out_info(compute_deconvolution_shape(*input->info(), stride_x, stride_y, inner_border_right, inner_border_top, info), 1, input->info()->data_type(),
input->info()->fixed_point_position());
_scaled_output.allocator()->init(scale_out_info);
// setup the function to convolve the upscaled output
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
_conv_f.configure(&_scaled_output, weights, bias, output, conv_info);
_scaled_output.allocator()->allocate();
}
void NEDeconvolutionLayer::run()
{
_memory_group.acquire();
// Initialize _scaled_output buffer
const int width_in = _input->info()->dimension(0);
const int height_in = _input->info()->dimension(1);
const int width_scaled = _scaled_output.info()->dimension(0);
const int height_scaled = _scaled_output.info()->dimension(1);
const int num_2d_slices = _input->info()->tensor_shape().total_size() / (width_in * height_in);
const int stride_x = _info.stride().first;
const int stride_y = _info.stride().second;
std::fill_n(reinterpret_cast<float *>(_scaled_output.buffer()), _scaled_output.info()->tensor_shape().total_size(), 0.f);
// scaled_output is the input for the forward convolution. We copy the input elements to scaled_output
// and insert rows and columns with zeroes depending on the stride values.
for(int slice = 0; slice < num_2d_slices; ++slice)
{
const int start_x = _info.pad().first;
const int start_y = _inner_border.second + _info.pad().second;
const int end_y = height_scaled - _info.pad().second;
const int end_x = width_scaled - _inner_border.first - _info.pad().first;
for(int yi = start_y, in_y = 0; yi < end_y; yi += stride_y, in_y++)
{
for(int xi = start_x, in_x = 0; xi < end_x; xi += stride_x, in_x++)
{
const auto in = *(reinterpret_cast<float *>(_input->buffer() + _input->info()->offset_element_in_bytes(Coordinates(in_x, in_y, slice))));
*(reinterpret_cast<float *>(_scaled_output.buffer() + _scaled_output.info()->offset_element_in_bytes(Coordinates(xi, yi, slice)))) = in;
}
}
}
_conv_f.run();
_memory_group.release();
}
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