| /* |
| * 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/NEON/functions/NEConvolutionLayer.h" |
| |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "support/MemorySupport.h" |
| |
| #include <cmath> |
| #include <tuple> |
| #include <utility> |
| |
| namespace arm_compute |
| { |
| NEConvolutionLayer::NEConvolutionLayer(std::shared_ptr<IMemoryManager> 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, unsigned int num_groups) |
| { |
| // Perform validate step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_UNUSED(num_groups); |
| 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, num_groups)); |
| |
| switch(NEConvolutionLayer::get_convolution_method(input->info(), weights->info(), output->info(), conv_info, weights_info, dilation, act_info, enable_fast_math)) |
| { |
| case ConvolutionMethod::WINOGRAD: |
| { |
| auto f = arm_compute::support::cpp14::make_unique<NEWinogradConvolutionLayer>(_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<NEGEMMConvolutionLayer>(_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<NEDirectConvolutionLayer>(_memory_manager); |
| f->configure(input, weights, biases, output, conv_info, act_info); |
| _function = std::move(f); |
| break; |
| } |
| case ConvolutionMethod::FFT: |
| { |
| auto f = arm_compute::support::cpp14::make_unique<NEFFTConvolutionLayer>(_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, unsigned int num_groups) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG((num_groups != 1), "Grouping (num_groups != 1) is not supported on NEON"); |
| |
| switch(NEConvolutionLayer::get_convolution_method(input, weights, output, conv_info, weights_info, dilation, act_info, enable_fast_math)) |
| { |
| 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 Direct Convolution |
| ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayer::validate(input, weights, biases, output, conv_info, act_info)); |
| break; |
| case ConvolutionMethod::FFT: |
| // Validate FFT-based convolution layer |
| ARM_COMPUTE_RETURN_ON_ERROR(NEFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info)); |
| break; |
| 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); |
| |
| const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); |
| const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); |
| |
| /* Input spatial dims, kernel size, IFM/OFM, conv info*/ |
| using ConvolutionConfiguration = std::tuple<Size2D, Size2D, Size2D, PadStrideInfo>; |
| using ConfigurationMethod = std::pair<ConvolutionConfiguration, ConvolutionMethod>; |
| |
| const std::vector<ConfigurationMethod> known_configs = |
| { |
| // Alexnet |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(27U, 27U), Size2D(5U, 5U), Size2D(48U, 128U), PadStrideInfo(1U, 1U, 2U, 2U)), ConvolutionMethod::GEMM), |
| // VGG16 / VGG19 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 64U), PadStrideInfo(1U, 1U, 1U, 1U)), ConvolutionMethod::GEMM), |
| // Mobilenet 224 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(224U, 224U), Size2D(3U, 3U), Size2D(3U, 32U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)), ConvolutionMethod::GEMM), |
| // Mobilenet 160 |
| ConfigurationMethod(ConvolutionConfiguration(Size2D(160U, 160U), Size2D(3U, 3U), Size2D(3U, 24U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR)), ConvolutionMethod::GEMM) |
| }; |
| |
| const auto find_config = [&](ConfigurationMethod c) |
| { |
| const ConvolutionConfiguration config = c.first; |
| const PadStrideInfo info = std::get<3>(config); |
| |
| return std::get<0>(config) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) && std::get<1>(config) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) |
| && std::get<2>(config) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() |
| && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride(); |
| }; |
| |
| std::vector<ConfigurationMethod>::const_iterator found; |
| if((found = std::find_if(known_configs.begin(), known_configs.end(), find_config)) != known_configs.end()) |
| { |
| return (*found).second; |
| } |
| |
| if(dilation != Size2D(1U, 1U)) |
| { |
| return ConvolutionMethod::GEMM; |
| } |
| else |
| { |
| // SRGAN |
| // Output might not be initialized when it is an internal tensor of the layer using the convolution |
| if(input->total_size() > 1e7 && (weights->dimension(idx_h) > 7) |
| && (NEDirectConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info))) |
| { |
| return ConvolutionMethod::DIRECT; |
| } |
| if((weights->dimension(idx_h) > 7) && (input->dimension(idx_c) > output->dimension(idx_c)) && (NEFFTConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info))) |
| { |
| return ConvolutionMethod::FFT; |
| } |
| if(input->dimension(idx_c) < 16) |
| { |
| return ConvolutionMethod::GEMM; |
| } |
| |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| // This heuristics only applies to F16 data type on A55r1 |
| if(NEScheduler::get().cpu_info().get_cpu_model() == CPUModel::A55r1 && enable_fast_math && input->data_type() == DataType::F16) |
| { |
| // Exclude known bad winograd configs (and defaults to GEMM) |
| const std::vector<ConvolutionConfiguration> known_bad_winograd_f16_with_fastmath_configs = |
| { |
| // Squeezenet_V1_1 fire2 and fire3 |
| ConvolutionConfiguration(Size2D(56U, 56U), Size2D(3U, 3U), Size2D(16U, 64U), PadStrideInfo(1U, 1U, 1U, 1U)), |
| // Squeezenet_V1_1 fire6 and fire7 |
| ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(48U, 192U), PadStrideInfo(1U, 1U, 1U, 1U)), |
| // Squeezenet_V1_1 fire8 and fire9 |
| ConvolutionConfiguration(Size2D(14U, 14U), Size2D(3U, 3U), Size2D(64U, 256U), PadStrideInfo(1U, 1U, 1U, 1U)), |
| }; |
| const auto find_conv_config = [&](ConvolutionConfiguration c) |
| { |
| const PadStrideInfo info = std::get<3>(c); |
| |
| return std::get<0>(c) == Size2D(input->dimension(idx_w), input->dimension(idx_h)) && std::get<1>(c) == Size2D(weights->dimension(idx_w), weights->dimension(idx_h)) |
| && std::get<2>(c) == Size2D(weights->dimension(idx_c), weights->dimension(3)) && info.pad_top() == conv_info.pad_top() && info.pad_right() == conv_info.pad_right() |
| && info.pad_bottom() == conv_info.pad_bottom() && info.pad_left() == conv_info.pad_left() && info.stride() == conv_info.stride(); |
| }; |
| |
| bool found_bad = std::find_if(known_bad_winograd_f16_with_fastmath_configs.begin(), known_bad_winograd_f16_with_fastmath_configs.end(), |
| find_conv_config) |
| != known_bad_winograd_f16_with_fastmath_configs.end(); |
| if(found_bad) |
| { |
| return ConvolutionMethod::GEMM; |
| } |
| } |
| #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| return bool(NEWinogradConvolutionLayer::validate(input, weights, nullptr, output, conv_info, act_info, enable_fast_math)) ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; |
| } |
| } |
| |
| void NEConvolutionLayer::run() |
| { |
| prepare(); |
| _function->run(); |
| } |
| |
| void NEConvolutionLayer::prepare() |
| { |
| _function->prepare(); |
| } |
| } // namespace arm_compute |