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/*
* Copyright (c) 2017-2020 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/CLDeconvolutionLayer.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include <cmath>
#include <memory>
#include <tuple>
using namespace arm_compute;
using namespace arm_compute::misc::shape_calculator;
CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_manager(std::move(memory_manager)), _function()
{
}
void CLDeconvolutionLayer::configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
configure(CLKernelLibrary::get().get_compile_context(), input, weights, bias, output, deconv_info, weights_info);
}
void CLDeconvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
switch(CLDeconvolutionLayer::get_deconvolution_method(input->info(), weights->info(), nullptr, output->info(), deconv_info, weights_info))
{
case DeconvolutionMethod::DIRECT:
{
auto f = arm_compute::support::cpp14::make_unique<CLDirectDeconvolutionLayer>();
f->configure(compile_context, input, weights, bias, output, deconv_info, weights_info);
_function = std::move(f);
break;
}
case DeconvolutionMethod::GEMM:
{
auto f = arm_compute::support::cpp14::make_unique<CLGEMMDeconvolutionLayer>(_memory_manager);
f->configure(compile_context, input, weights, bias, output, deconv_info);
_function = std::move(f);
break;
}
default:
ARM_COMPUTE_ERROR("Not supported.");
break;
}
}
Status CLDeconvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
switch(CLDeconvolutionLayer::get_deconvolution_method(input, weights, bias, output, deconv_info, weights_info))
{
case DeconvolutionMethod::DIRECT:
{
// Validate direct convolution layer
ARM_COMPUTE_RETURN_ON_ERROR(CLDirectDeconvolutionLayer::validate(input, weights, bias, output, deconv_info, weights_info));
break;
}
case DeconvolutionMethod::GEMM:
{
// Validate gemm-based convolution layer
ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMDeconvolutionLayer::validate(input, weights, bias, output, deconv_info));
break;
}
default:
ARM_COMPUTE_ERROR("Not supported.");
break;
}
return Status{};
}
DeconvolutionMethod CLDeconvolutionLayer::get_deconvolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &deconv_info,
const WeightsInfo &weights_info)
{
ARM_COMPUTE_UNUSED(output, bias, weights_info);
const DataLayout data_layout = input->data_layout();
const size_t idx_w = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const size_t idx_h = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
if(weights->dimension(idx_w) != deconv_info.stride().first || weights->dimension(idx_h) != deconv_info.stride().second)
{
return DeconvolutionMethod::DIRECT;
}
return DeconvolutionMethod::GEMM;
}
void CLDeconvolutionLayer::run()
{
prepare();
_function->run();
}
void CLDeconvolutionLayer::prepare()
{
_function->prepare();
}
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