/* * 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/NEConvolutionLayer.h" #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "support/ToolchainSupport.h" #include #include #include namespace arm_compute { NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr memory_manager) //NOLINT : _memory_manager(std::move(memory_manager)), _function() { } void NEConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math) { // Perform validate step ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_ERROR_THROW_ON(NEConvolutionLayer::validate(input->info(), weights->info(), ((biases != nullptr) ? biases->info() : nullptr), output->info(), conv_info, weights_info, dilation, act_info, enable_fast_math)); switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, weights_info, dilation, act_info)) { case ConvolutionMethod::WINOGRAD: { auto f = arm_compute::support::cpp14::make_unique(_memory_manager); f->configure(input, weights, biases, output, conv_info, act_info, enable_fast_math); _function = std::move(f); break; } case ConvolutionMethod::GEMM: { auto f = arm_compute::support::cpp14::make_unique(_memory_manager); f->configure(input, weights, biases, output, conv_info, weights_info, dilation, act_info); _function = std::move(f); break; } case ConvolutionMethod::DIRECT: { auto f = arm_compute::support::cpp14::make_unique(_memory_manager); f->configure(input, weights, biases, output, conv_info, act_info); _function = std::move(f); break; } default: ARM_COMPUTE_ERROR("Not supported."); break; } } Status NEConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math) { switch(NEConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info)) { case ConvolutionMethod::WINOGRAD: //Validate Winograd ARM_COMPUTE_RETURN_ON_ERROR(NEWinogradConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math)); break; case ConvolutionMethod::GEMM: //Validate Gemm-based Convolution ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMConvolutionLayer::validate(input, weights, biases, output, conv_info, weights_info, dilation, act_info)); break; case ConvolutionMethod::DIRECT: //Validate Gemm-based Convolution ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info)); default: ARM_COMPUTE_ERROR("Not supported."); break; } return Status{}; } ConvolutionMethod NEConvolutionLayer::get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info, bool enable_fast_math) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, weights); ARM_COMPUTE_UNUSED(weights_info); if(dilation != Size2D(1U, 1U) || Scheduler::get().cpu_info().get_cpu_model() == CPUModel::A53 || input->dimension(get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL)) <= 16) { return ConvolutionMethod::GEMM; } return bool(NEWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; } void NEConvolutionLayer::run() { _function->run(); } } // namespace arm_compute