| /* |
| * Copyright (c) 2017-2023 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 "src/core/CL/ICLKernel.h" |
| #include "src/gpu/cl/IClOperator.h" |
| #include "src/gpu/cl/operators/ClTransposedConvolution.h" |
| |
| #include "src/common/utils/Log.h" |
| |
| #include <cmath> |
| #include <memory> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::misc::shape_calculator; |
| |
| struct CLDeconvolutionLayer::Impl |
| { |
| const ICLTensor *src{ nullptr }; |
| const ICLTensor *weights{ nullptr }; |
| const ICLTensor *biases{ nullptr }; |
| ICLTensor *dst{ nullptr }; |
| std::unique_ptr<opencl::IClOperator> op{ nullptr }; |
| }; |
| |
| CLDeconvolutionLayer::~CLDeconvolutionLayer() = default; |
| |
| CLDeconvolutionLayer::CLDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_manager(std::move(memory_manager)), _function(), _impl(std::make_unique<Impl>()) |
| { |
| } |
| |
| 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); |
| ARM_COMPUTE_LOG_PARAMS(input, weights, bias, output, deconv_info, weights_info); |
| |
| switch(CLDeconvolutionLayer::get_deconvolution_method(input->info(), weights->info(), nullptr, output->info(), deconv_info, weights_info)) |
| { |
| case DeconvolutionMethod::DIRECT: |
| { |
| auto op = std::make_unique<opencl::ClTransposedConvolution>(); |
| op->configure(compile_context, input->info(), weights->info(), bias != nullptr ? bias->info() : nullptr, output->info(), deconv_info); |
| |
| _impl->src = input; |
| _impl->weights = weights; |
| _impl->biases = bias; |
| _impl->dst = output; |
| |
| _impl->op = std::move(op); |
| break; |
| } |
| case DeconvolutionMethod::UPSCALE_CONV2D: |
| { |
| auto f = std::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 = std::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 transposed convolution operator |
| ARM_COMPUTE_RETURN_ON_ERROR(opencl::ClTransposedConvolution::validate(input, weights, bias, output, deconv_info)); |
| break; |
| } |
| case DeconvolutionMethod::UPSCALE_CONV2D: |
| { |
| // 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); |
| |
| if(is_data_type_quantized_per_channel(weights->data_type())) |
| { |
| return DeconvolutionMethod::UPSCALE_CONV2D; |
| } |
| |
| 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) |
| { |
| if(input->data_layout() == DataLayout::NHWC) |
| { |
| return DeconvolutionMethod::DIRECT; |
| } |
| else |
| { |
| return DeconvolutionMethod::UPSCALE_CONV2D; |
| } |
| } |
| |
| return DeconvolutionMethod::GEMM; |
| } |
| |
| void CLDeconvolutionLayer::run() |
| { |
| prepare(); |
| |
| if(_impl->op != nullptr) |
| { |
| // Optimized Operator will be used |
| ITensorPack pack; |
| |
| pack.add_tensor(TensorType::ACL_SRC_0, _impl->src); |
| pack.add_tensor(TensorType::ACL_SRC_1, _impl->weights); |
| pack.add_tensor(TensorType::ACL_SRC_2, _impl->biases); |
| pack.add_tensor(TensorType::ACL_DST, _impl->dst); |
| |
| _impl->op->run(pack); |
| } |
| else |
| { |
| _function->run(); |
| } |
| } |
| |
| void CLDeconvolutionLayer::prepare() |
| { |
| if(_impl->op == nullptr) |
| { |
| _function->prepare(); |
| } |
| } |