/* * Copyright (c) 2018-2020 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/graph/backends/NEON/NEFunctionFactory.h" #include "arm_compute/core/utils/misc/Cast.h" #include "arm_compute/graph/Graph.h" #include "arm_compute/graph/GraphContext.h" #include "arm_compute/graph/Logger.h" #include "arm_compute/graph/TypePrinter.h" #include "arm_compute/graph/backends/FunctionHelpers.h" #include "arm_compute/graph/backends/Utils.h" #include "arm_compute/graph/nodes/Nodes.h" #include "arm_compute/runtime/CPP/CPPFunctions.h" #include "arm_compute/runtime/NEON/NEFunctions.h" #include "support/ToolchainSupport.h" using namespace arm_compute::utils::cast; namespace arm_compute { namespace graph { namespace backends { /** Target specific information structure used to pass information to the layer templates */ struct NETargetInfo { using TensorType = arm_compute::ITensor; using TensorConcreteType = arm_compute::Tensor; static Target TargetType; }; Target NETargetInfo::TargetType = Target::NEON; /** Collection of NEON convolution functions */ struct NEConvolutionLayerFunctions { using GenericConvolutionLayer = NEConvolutionLayer; using GEMMConvolutionLayer = NEGEMMConvolutionLayer; using DirectConvolutionLayer = NEDirectConvolutionLayer; using WinogradConvolutionLayer = NEWinogradConvolutionLayer; }; /** Collection of NEON element-wise functions */ struct NEEltwiseFunctions { using Addition = NEArithmeticAddition; using Subtraction = NEArithmeticSubtraction; using Multiplication = NEPixelWiseMultiplication; }; /** Collection of NEON unary element-wise functions */ struct NEUnaryEltwiseFunctions { using Exp = NEExpLayer; }; /** Function and tensor types to be used inside a NEON fused convolution/batch normalization layer */ struct NEFusedLayerTypes { using ConvolutionLayer = NEConvolutionLayer; using DepthwiseConvolutionLayer = NEDepthwiseConvolutionLayer; using FuseBatchNormalization = NEFuseBatchNormalization; }; namespace detail { template <> std::unique_ptr create_normalization_layer(NormalizationLayerNode &node, GraphContext &ctx) { validate_node(node, 1 /* expected inputs */, 1 /* expected outputs */); // Extract IO and info NETargetInfo::TensorType *input = get_backing_tensor(node.input(0)); NETargetInfo::TensorType *output = get_backing_tensor(node.output(0)); const NormalizationLayerInfo norm_info = node.normalization_info(); ARM_COMPUTE_ERROR_ON(input == nullptr); ARM_COMPUTE_ERROR_ON(output == nullptr); // Create and configure function auto func = support::cpp14::make_unique(get_memory_manager(ctx, NETargetInfo::TargetType)); func->configure(input, output, norm_info); // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() << " Type: " << node.type() << " Target: " << NETargetInfo::TargetType << " Data Type: " << input->info()->data_type() << " Input shape: " << input->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() << " Normalization info: " << norm_info.type() << std::endl); return RETURN_UNIQUE_PTR(func); } } // namespace detail std::unique_ptr NEFunctionFactory::create(INode *node, GraphContext &ctx) { if(node == nullptr) { return nullptr; } NodeType type = node->type(); switch(type) { case NodeType::ActivationLayer: return detail::create_activation_layer(*polymorphic_downcast(node)); case NodeType::BatchNormalizationLayer: return detail::create_batch_normalization_layer(*polymorphic_downcast(node)); case NodeType::ChannelShuffleLayer: return detail::create_channel_shuffle_layer(*polymorphic_downcast(node)); case NodeType::ConvolutionLayer: return detail::create_convolution_layer(*polymorphic_downcast(node), ctx); case NodeType::DeconvolutionLayer: return detail::create_deconvolution_layer(*polymorphic_downcast(node), ctx); case NodeType::ConcatenateLayer: return detail::create_concatenate_layer(*polymorphic_downcast(node)); case NodeType::DepthwiseConvolutionLayer: return detail::create_depthwise_convolution_layer(*polymorphic_downcast(node)); case NodeType::DequantizationLayer: return detail::create_dequantization_layer(*polymorphic_downcast(node)); case NodeType::DetectionOutputLayer: return detail::create_detection_output_layer(*polymorphic_downcast(node)); case NodeType::DetectionPostProcessLayer: return detail::create_detection_post_process_layer(*polymorphic_downcast(node)); case NodeType::EltwiseLayer: return detail::create_eltwise_layer(*polymorphic_downcast(node)); case NodeType::UnaryEltwiseLayer: return detail::create_unary_eltwise_layer(*polymorphic_downcast(node)); case NodeType::FlattenLayer: return detail::create_flatten_layer(*polymorphic_downcast(node)); case NodeType::FullyConnectedLayer: return detail::create_fully_connected_layer(*polymorphic_downcast(node), ctx); case NodeType::FusedConvolutionBatchNormalizationLayer: return detail::create_fused_convolution_batch_normalization_layer(*polymorphic_downcast(node), ctx); case NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer: return detail::create_fused_depthwise_convolution_batch_normalization_layer(*polymorphic_downcast(node), ctx); case NodeType::NormalizationLayer: return detail::create_normalization_layer(*polymorphic_downcast(node), ctx); case NodeType::PadLayer: return detail::create_pad_layer(*polymorphic_downcast(node)); case NodeType::PermuteLayer: return detail::create_permute_layer(*polymorphic_downcast(node)); case NodeType::PoolingLayer: return detail::create_pooling_layer(*polymorphic_downcast(node)); case NodeType::PReluLayer: return detail::create_prelu_layer(*polymorphic_downcast(node)); case NodeType::PrintLayer: return detail::create_print_layer(*polymorphic_downcast(node)); case NodeType::PriorBoxLayer: return detail::create_priorbox_layer(*polymorphic_downcast(node)); case NodeType::QuantizationLayer: return detail::create_quantization_layer(*polymorphic_downcast(node)); case NodeType::ReorgLayer: return detail::create_reorg_layer(*polymorphic_downcast(node)); case NodeType::ReshapeLayer: return detail::create_reshape_layer(*polymorphic_downcast(node)); case NodeType::ResizeLayer: return detail::create_resize_layer(*polymorphic_downcast(node)); case NodeType::SoftmaxLayer: return detail::create_softmax_layer(*polymorphic_downcast(node), ctx); case NodeType::StackLayer: return detail::create_stack_layer(*polymorphic_downcast(node)); case NodeType::UpsampleLayer: return detail::create_upsample_layer(*polymorphic_downcast(node), ctx); case NodeType::YOLOLayer: return detail::create_yolo_layer(*polymorphic_downcast(node), ctx); default: return nullptr; } } } // namespace backends } // namespace graph } // namespace arm_compute