diff options
| -rw-r--r-- | arm_compute/graph/Nodes.h | 1 | ||||
| -rw-r--r-- | arm_compute/graph/Types.h | 1 | ||||
| -rw-r--r-- | arm_compute/graph/nodes/ResidualLayer.h | 76 | ||||
| -rw-r--r-- | examples/graph_resnet50.cpp | 233 | ||||
| -rw-r--r-- | src/graph/Graph.cpp | 1 | ||||
| -rw-r--r-- | src/graph/nodes/ResidualLayer.cpp | 199 | ||||
| -rw-r--r-- | src/graph/operations/CLSimpleOperations.cpp | 30 | ||||
| -rw-r--r-- | src/graph/operations/NESimpleOperations.cpp | 30 |
8 files changed, 569 insertions, 2 deletions
diff --git a/arm_compute/graph/Nodes.h b/arm_compute/graph/Nodes.h index 4390c59959..3009a24fcb 100644 --- a/arm_compute/graph/Nodes.h +++ b/arm_compute/graph/Nodes.h @@ -39,6 +39,7 @@ #include "arm_compute/graph/nodes/PoolingLayer.h" #include "arm_compute/graph/nodes/QuantizationLayer.h" #include "arm_compute/graph/nodes/ReshapeLayer.h" +#include "arm_compute/graph/nodes/ResidualLayer.h" #include "arm_compute/graph/nodes/SoftmaxLayer.h" #endif /* __ARM_COMPUTE_GRAPH_NODES_H__ */ diff --git a/arm_compute/graph/Types.h b/arm_compute/graph/Types.h index 3ec0c7a5cf..a5d6ae8459 100644 --- a/arm_compute/graph/Types.h +++ b/arm_compute/graph/Types.h @@ -91,6 +91,7 @@ enum class ConvolutionMethodHint enum class OperationType { ActivationLayer, + ArithmeticAddition, BatchNormalizationLayer, ConvolutionLayer, DepthConvertLayer, diff --git a/arm_compute/graph/nodes/ResidualLayer.h b/arm_compute/graph/nodes/ResidualLayer.h new file mode 100644 index 0000000000..1eecf6f996 --- /dev/null +++ b/arm_compute/graph/nodes/ResidualLayer.h @@ -0,0 +1,76 @@ +/* + * 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. + */ +#ifndef __ARM_COMPUTE_GRAPH_RESIDUAL_LAYER_H__ +#define __ARM_COMPUTE_GRAPH_RESIDUAL_LAYER_H__ + +#include "arm_compute/graph/GraphContext.h" +#include "arm_compute/graph/INode.h" +#include "arm_compute/graph/ITensorObject.h" +#include "arm_compute/graph/SubGraph.h" +#include "arm_compute/graph/Types.h" + +#include "arm_compute/core/utils/misc/utility.h" + +#include <vector> + +namespace arm_compute +{ +namespace graph +{ +/** Branch Layer node */ +class ResidualLayer final : public INode +{ +public: + /** Default Constructor + * + * @param[in] sub_graph1 First graph branch + * @param[in] sub_graph2 Second graph branch + */ + template <typename... Ts> + ResidualLayer(SubGraph &&sub_graph1, SubGraph &&sub_graph2) + : _sub_graphs() + { + _sub_graphs.push_back(arm_compute::support::cpp14::make_unique<SubGraph>(std::move(sub_graph1))); + _sub_graphs.push_back(arm_compute::support::cpp14::make_unique<SubGraph>(std::move(sub_graph2))); + } + /** Default Constructor + * + * @param[in] sub_graph Sub graph + */ + template <typename... Ts> + ResidualLayer(SubGraph &&sub_graph) + : _sub_graphs() + { + _sub_graphs.push_back(arm_compute::support::cpp14::make_unique<SubGraph>(std::move(sub_graph))); + } + + // Inherited methods overriden: + std::unique_ptr<arm_compute::IFunction> instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) override; + +private: + std::vector<std::unique_ptr<SubGraph>> _sub_graphs; +}; +} // namespace graph +} // namespace arm_compute +#endif /* __ARM_COMPUTE_GRAPH_RESIDUAL_LAYER_H__ */ diff --git a/examples/graph_resnet50.cpp b/examples/graph_resnet50.cpp new file mode 100644 index 0000000000..88f58bf09e --- /dev/null +++ b/examples/graph_resnet50.cpp @@ -0,0 +1,233 @@ +/* + * 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/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include <cstdlib> + +using namespace arm_compute::utils; +using namespace arm_compute::graph; +using namespace arm_compute::graph_utils; + +/** Example demonstrating how to implement Microsoft's ResNet50 network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +class GraphResNet50Example : public Example +{ +public: + void do_setup(int argc, char **argv) override + { + std::string data_path; /* Path to the trainable data */ + std::string image; /* Image data */ + std::string label; /* Label data */ + + // Create a preprocessor object + const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; + std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb, + false /* Do not convert to BGR */); + + // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON + const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; + TargetHint target_hint = set_target_hint(int_target_hint); + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 3) + { + data_path = argv[2]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n"; + std::cout << "No image provided: using random values\n\n"; + } + else if(argc == 4) + { + data_path = argv[2]; + image = argv[3]; + std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; + std::cout << "No text file with labels provided: skipping output accessor\n\n"; + } + else + { + data_path = argv[2]; + image = argv[3]; + label = argv[4]; + } + + // Initialize the graph + graph.graph_init(int_target_hint == 2); + + graph << target_hint + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32), + get_input_accessor(image, std::move(preprocessor), false /* Do not convert to BGR */)) + << ConvolutionLayer( + 7U, 7U, 64U, + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(2, 2, 3, 3)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"), + 0.0000100099996416f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))); + + add_residual_block(data_path, "block1", 64, 3, 2); + add_residual_block(data_path, "block2", 128, 4, 2); + add_residual_block(data_path, "block3", 256, 6, 2); + add_residual_block(data_path, "block4", 512, 3, 1); + + graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)) + << ConvolutionLayer( + 1U, 1U, 1000U, + get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy"), + get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + << FlattenLayer() + << SoftmaxLayer() + << Tensor(get_output_accessor(label, 5)); + } + void do_run() override + { + // Run graph + graph.run(); + } + +private: + Graph graph{}; + + void add_residual_block(const std::string &data_path, const std::string &name, unsigned int base_depth, unsigned int num_units, unsigned int stride) + { + for(unsigned int i = 0; i < num_units; ++i) + { + std::stringstream unit; + unit << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_"; + std::string unit_name = unit.str(); + + unsigned int middle_stride = 1; + + if(i == (num_units - 1)) + { + middle_stride = stride; + } + + SubGraph right; + right << ConvolutionLayer( + 1U, 1U, base_depth, + get_weights_accessor(data_path, unit_name + "conv1_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "conv1_BatchNorm_beta.npy"), + 0.0000100099996416f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer( + 3U, 3U, base_depth, + get_weights_accessor(data_path, unit_name + "conv2_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(middle_stride, middle_stride, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "conv2_BatchNorm_beta.npy"), + 0.0000100099996416f) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + + << ConvolutionLayer( + 1U, 1U, base_depth * 4, + get_weights_accessor(data_path, unit_name + "conv3_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "conv3_BatchNorm_beta.npy"), + 0.0000100099996416f); + + if(i == 0) + { + SubGraph left; + left << ConvolutionLayer( + 1U, 1U, base_depth * 4, + get_weights_accessor(data_path, unit_name + "shortcut_weights.npy"), + std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, unit_name + "shortcut_BatchNorm_beta.npy"), + 0.0000100099996416f); + + graph << ResidualLayer(std::move(left), std::move(right)); + } + else if(middle_stride > 1) + { + SubGraph left; + left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)) + // TODO (alegil01) : Remove once we understand why a single node graph does not run in CL + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f, 0.f)); + + graph << ResidualLayer(std::move(left), std::move(right)); + } + else + { + graph << ResidualLayer(std::move(right)); + } + + graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + } + } +}; + +/** Main program for ResNet50 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example<GraphResNet50Example>(argc, argv); +} diff --git a/src/graph/Graph.cpp b/src/graph/Graph.cpp index 98d95904dc..b6c6822c36 100644 --- a/src/graph/Graph.cpp +++ b/src/graph/Graph.cpp @@ -77,6 +77,7 @@ Graph::~Graph() //NOLINT Graph::Graph() : _pimpl{ new Private() } { + graph_init(); } void Graph::graph_init(const bool use_cl_tuner) diff --git a/src/graph/nodes/ResidualLayer.cpp b/src/graph/nodes/ResidualLayer.cpp new file mode 100644 index 0000000000..87404f9e1f --- /dev/null +++ b/src/graph/nodes/ResidualLayer.cpp @@ -0,0 +1,199 @@ +/* + * 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/graph/nodes/ResidualLayer.h" + +#include "arm_compute/graph/Error.h" +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/NodeContext.h" +#include "arm_compute/graph/OperationRegistry.h" +#include "arm_compute/graph/SubGraph.h" +#include "arm_compute/graph/Tensor.h" +#include "arm_compute/runtime/IFunction.h" +#include "support/ToolchainSupport.h" +#include "utils/Utils.h" + +#include <memory> +#include <tuple> +#include <vector> + +using namespace arm_compute::graph; + +/** Residual function */ +class ResidualFunction final : public arm_compute::IFunction +{ +public: + /** Default Constructor */ + ResidualFunction(GraphContext &ctx, ITensorObject *output) + : _ctx(ctx), _input(nullptr), _output(output), _func(nullptr), _graphs(), _graph_outputs() + { + } + + /** Prevent instances from being copy constructed */ + ResidualFunction(const ResidualFunction &) = delete; + /** Prevent instances from being copy assigned */ + const ResidualFunction &operator=(const ResidualFunction &) = delete; + /** Prevent instances from being move constructed */ + ResidualFunction(ResidualFunction &&) = delete; + /** Prevent instances from being move assigned */ + ResidualFunction &operator=(ResidualFunction &&) = delete; + /** Default destructor */ + ~ResidualFunction() override = default; + + /** Set the input (when using only one sub graph) + * + * @param[in] input Input to set + */ + void set_input(std::unique_ptr<ITensorObject> input) + { + _input = std::move(input); + } + + /** Registers graph to be executed by the residual function + * + * @param[in] graph Graph to register + * @param[in] output Output to register + */ + void register_graph(std::unique_ptr<Graph> graph, std::unique_ptr<ITensorObject> output) + { + _graphs.push_back(std::move(graph)); + _graph_outputs.push_back(std::move(output)); + } + + /** Configure the function */ + void configure() + { + ARM_COMPUTE_ERROR_ON(_graphs.size() < 1 || _graphs.size() > 2); + TargetHint target_hint = _ctx.hints().target_hint(); + + // Create node context + NodeContext node_ctx(OperationType::ArithmeticAddition); + node_ctx.set_target(target_hint); + + if(_graphs.size() == 1) + { + arm_compute::ITensor *in = _input->tensor(); + node_ctx.add_input(in); + } + + for(auto &o : _graph_outputs) + { + arm_compute::ITensor *in = o->tensor(); + node_ctx.add_input(in); + } + + arm_compute::ITensor *out = _output->tensor(); + auto_init_if_empty(*out->info(), *_graph_outputs[0]->tensor()->info()); + node_ctx.add_output(out); + + _func = OperationRegistry::get().find_operation(OperationType::ArithmeticAddition, target_hint)->configure(node_ctx); + + for(auto &o : _graph_outputs) + { + o->allocate(); + } + } + + // Inherited methods overriden: + void run() override + { + ARM_COMPUTE_ERROR_ON(_graphs.size() < 1 || _graphs.size() > 2); + + for(auto &g : _graphs) + { + ARM_COMPUTE_ERROR_ON(g.get() == nullptr); + g->run(); + } + + _func->run(); + } + +private: + GraphContext _ctx; + std::unique_ptr<ITensorObject> _input; + ITensorObject *_output; + std::unique_ptr<arm_compute::IFunction> _func; + std::vector<std::unique_ptr<Graph>> _graphs; + std::vector<std::unique_ptr<ITensorObject>> _graph_outputs; +}; + +std::unique_ptr<arm_compute::IFunction> ResidualLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output) +{ + ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output); + ARM_COMPUTE_ERROR_ON(dynamic_cast<Tensor *>(input) == nullptr); + ARM_COMPUTE_ERROR_ON(dynamic_cast<Tensor *>(output) == nullptr); + + // Create residual function + auto func = arm_compute::support::cpp14::make_unique<ResidualFunction>(ctx, output); + + if(_sub_graphs.size() == 1) + { + std::unique_ptr<ITensorObject> original_in; + original_in = arm_compute::support::cpp14::make_unique<SubTensor>(*dynamic_cast<Tensor *>(input), + input->tensor()->info()->tensor_shape(), + Coordinates()); + func->set_input(std::move(original_in)); + } + + // Constuct all sub-graphs given the input/output + for(auto &sg : _sub_graphs) + { + ARM_COMPUTE_ERROR_ON(sg.get() == nullptr); + + // IO buffers + std::unique_ptr<ITensorObject> in; + std::unique_ptr<ITensorObject> out; + std::unique_ptr<ITensorObject> func_in; + + // Create input sub-tensor + if(!sg->has_input()) + { + in = arm_compute::support::cpp14::make_unique<SubTensor>(*dynamic_cast<Tensor *>(input), + input->tensor()->info()->tensor_shape(), + Coordinates()); + } + + // Create output sub-tensor + if(!sg->has_output()) + { + ITensorInfo *info = input->tensor()->info(); + func_in = arm_compute::support::cpp14::make_unique<Tensor>(TensorInfo(info->num_channels(), info->data_type(), info->fixed_point_position())); + func_in->set_target(ctx.hints().target_hint()); + out = arm_compute::support::cpp14::make_unique<SubTensor>(func_in->tensor(), + TensorShape(), + Coordinates(0, 0, 0), + func_in->target(), + true); + } + + // Construct sub_graph + auto g = sg->construct(ctx, std::move(in), std::move(out)); + + // Register graph to function + func->register_graph(std::move(g), std::move(func_in)); + } + + func->configure(); + + return std::move(func); +} diff --git a/src/graph/operations/CLSimpleOperations.cpp b/src/graph/operations/CLSimpleOperations.cpp index 94e3fe15f7..fe56122009 100644 --- a/src/graph/operations/CLSimpleOperations.cpp +++ b/src/graph/operations/CLSimpleOperations.cpp @@ -66,6 +66,34 @@ REGISTER_SIMPLE_OPERATION(CLActivationLayerOperation, OPENCL, OperationType::Act return std::move(activation); } +/* Arithmetic addition */ +REGISTER_SIMPLE_OPERATION(CLArithmeticAdditionOperation, OPENCL, OperationType::ArithmeticAddition) +{ + ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2); + ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); + ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)) == nullptr); + ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)) == nullptr); + ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)) == nullptr); + + // Extract IO and info + auto *in1 = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(0)); + auto *in2 = dynamic_cast<arm_compute::ICLTensor *>(ctx.input(1)); + auto *out = dynamic_cast<arm_compute::ICLTensor *>(ctx.output(0)); + + auto addition = arm_compute::support::cpp14::make_unique<arm_compute::CLArithmeticAddition>(); + addition->configure(in1, in2, out, ConvertPolicy::SATURATE); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating CLArithmeticAddition" + << " Data Type: " << in1->info()->data_type() + << " Input 1 shape: " << in1->info()->tensor_shape() + << " Input 2 shape: " << in2->info()->tensor_shape() + << " Output shape: " << out->info()->tensor_shape() + << std::endl); + + return std::move(addition); +} + /* Batch Normalization Layer */ REGISTER_SIMPLE_OPERATION(CLBatchNormalizationLayerOperation, OPENCL, OperationType::BatchNormalizationLayer) { @@ -464,4 +492,4 @@ REGISTER_SIMPLE_OPERATION(CLSoftmaxLayerOperation, OPENCL, OperationType::Softma << std::endl); return std::move(smx); -}
\ No newline at end of file +} diff --git a/src/graph/operations/NESimpleOperations.cpp b/src/graph/operations/NESimpleOperations.cpp index 265bed6b7a..4154b9a59c 100644 --- a/src/graph/operations/NESimpleOperations.cpp +++ b/src/graph/operations/NESimpleOperations.cpp @@ -66,6 +66,34 @@ REGISTER_SIMPLE_OPERATION(NEActivationLayerOperation, NEON, OperationType::Activ return std::move(activation); } +/* Arithmetic addition */ +REGISTER_SIMPLE_OPERATION(NEArithmeticAdditionOperation, NEON, OperationType::ArithmeticAddition) +{ + ARM_COMPUTE_ERROR_ON(ctx.num_inputs() != 2); + ARM_COMPUTE_ERROR_ON(ctx.num_outputs() != 1); + ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(0)) == nullptr); + ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.input(1)) == nullptr); + ARM_COMPUTE_ERROR_ON(dynamic_cast<arm_compute::ITensor *>(ctx.output(0)) == nullptr); + + // Extract IO and info + auto *in1 = dynamic_cast<arm_compute::ITensor *>(ctx.input(0)); + auto *in2 = dynamic_cast<arm_compute::ITensor *>(ctx.input(1)); + auto *out = dynamic_cast<arm_compute::ITensor *>(ctx.output(0)); + + auto addition = arm_compute::support::cpp14::make_unique<arm_compute::NEArithmeticAddition>(); + addition->configure(in1, in2, out, ConvertPolicy::SATURATE); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiating NEArithmeticAddition" + << " Data Type: " << in1->info()->data_type() + << " Input 1 shape: " << in1->info()->tensor_shape() + << " Input 2 shape: " << in2->info()->tensor_shape() + << " Output shape: " << out->info()->tensor_shape() + << std::endl); + + return std::move(addition); +} + /* Batch Normalization Layer */ REGISTER_SIMPLE_OPERATION(NEBatchNormalizationLayerOperation, NEON, OperationType::BatchNormalizationLayer) { @@ -464,4 +492,4 @@ REGISTER_SIMPLE_OPERATION(NESoftmaxLayerOperation, NEON, OperationType::SoftmaxL << std::endl); return std::move(smx); -}
\ No newline at end of file +} |