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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/CL/functions/CLDeconvolutionLayer.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"
#include <memory>
#include <tuple>
using namespace arm_compute;
using namespace arm_compute::misc::shape_calculator;
CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT
: _memory_group(std::move(memory_manager)),
_scale_f(),
_conv_f(),
_scaled_output(),
_is_prepared(false)
{
}
Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != weights->dimension(1));
ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) < 1);
ARM_COMPUTE_RETURN_ERROR_ON(!info.padding_is_symmetric());
const unsigned int stride_x = info.stride().first;
const unsigned int stride_y = info.stride().second;
ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_right > stride_x - 1, "inner_border_right must be smaller than stride_x");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(inner_border_top > stride_y - 1, "inner_border_top must be smaller than stride_y");
auto out_dims = deconvolution_output_dimensions(input->dimension(0), input->dimension(1), weights->dimension(0), weights->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->tensor_shape(), weights->tensor_shape());
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, weights);
if(bias != nullptr)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
}
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimX) != output_shape.x(), "Output's width is invalid.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimY) != output_shape.y(), "Output's height is invalid.");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(output->dimension(Window::DimZ) != output_shape.z(), "Output's depth is invalid.");
TensorInfo scale_out_info(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_deconvolution_shape(*input, stride_x, stride_y, inner_border_right, inner_border_top,
info)));
const PadStrideInfo conv_info(1, 1, 0, 0, 0, 0, DimensionRoundingType::CEIL);
ARM_COMPUTE_RETURN_ON_ERROR(CLDeconvolutionLayerUpsample::validate(input, &scale_out_info, BorderSize(inner_border_right, inner_border_top), info));
ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayer::validate(&scale_out_info, weights, bias, output, conv_info, weights_info));
return Status{};
}
void CLDeconvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info,
unsigned int inner_border_right, unsigned int inner_border_top, const WeightsInfo &weights_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
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_top, inner_border_right, stride_x, stride_y);
const TensorShape output_shape = deconvolution_output_shape(out_dims, input->info()->tensor_shape(), weights->info()->tensor_shape());
// Output auto initialization if not yet initialized
auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
// Perform validation step
ARM_COMPUTE_ERROR_THROW_ON(CLDeconvolutionLayer::validate(input->info(), weights->info(), bias == nullptr ? nullptr : bias->info(), output->info(), info, inner_border_right, inner_border_top));
_is_prepared = false;
_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
TensorShape scale_out_shape(input->info()->tensor_shape());
const unsigned int out_x = input->info()->dimension(0) + (input->info()->dimension(0) - 1) * (stride_x - 1) + inner_border_right + 2 * info.pad().first;
const unsigned int out_y = input->info()->dimension(1) + (input->info()->dimension(1) - 1) * (stride_y - 1) + inner_border_top + 2 * info.pad().second;
scale_out_shape.set(0, out_x);
scale_out_shape.set(1, out_y);
TensorInfo scale_out_info(scale_out_shape, 1, input->info()->data_type());
_scaled_output.allocator()->init(scale_out_info);
_scale_f.configure(input, &_scaled_output, BorderSize(inner_border_top, inner_border_right), 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, weights_info);
_scaled_output.allocator()->allocate();
}
void CLDeconvolutionLayer::run()
{
prepare();
_memory_group.acquire();
_scale_f.run();
_conv_f.run();
_memory_group.release();
}
void CLDeconvolutionLayer::prepare()
{
if(!_is_prepared)
{
_conv_f.prepare();
_is_prepared = true;
}
}
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